October 2025 arXiv papers — page 179

Simon Schmidt, Sigurd A. L. Storgaard, Michael Walter, Yuming Zhao

In this article, we study a nonlocal game with two questions and three answers per player, which was first considered by Feige in 1991, and show that there is quantum advantage in this game. We prove that the game is a robust self-test for the $3$-dimensional maximally entangled state. Furthermore, we show that the game can be seen as the "or" of two games t

Shangqing Tu, Yaxuan Li, Yushi Bai, Lei Hou

Parallel scaling has emerged as a powerful paradigm to enhance reasoning capabilities in large language models (LLMs) by generating multiple Chain-of-Thought (CoT) traces simultaneously. However, this approach introduces significant computational inefficiency due to inter-trace redundancy -- our analysis reveals that over 80% of parallel reasoning traces yie

Jiacheng Liu, Peiliang Cai, Qinming Zhou, Yuqi Lin

The application of diffusion transformers is suffering from their significant inference costs. Recently, feature caching has been proposed to solve this problem by reusing features from previous timesteps, thereby skipping computation in future timesteps. However, previous feature caching assumes that features in adjacent timesteps are similar or continuous,

Yifei Xu, Jiaying Wu, Herun Wan, Yang Li

Hashtag trends ignite campaigns, shift public opinion, and steer millions of dollars in advertising spend, yet forecasting which tag goes viral is elusive. Classical regressors digest surface features but ignore context, while large language models (LLMs) excel at contextual reasoning but misestimate numbers. We present BuzzProphet, a reasoning-augmented has

Zhenlong Yuan, Xiangyan Qu, Chengxuan Qian, Rui Chen

Multimodal large language models (MLLMs) have demonstrated remarkable potential in bridging visual and textual reasoning, yet their reliance on text-centric priors often limits their ability to disentangle semantically similar actions in open-vocabulary scenarios. To address this, we propose Video-STAR, a framework that harmonizes contextual sub-motion decom

Jinsong Mao, Benjamin E. Ujcich, Shiqing Ma

Provenance plays a critical role in maintaining traceability of a system's actions for root cause analysis of security threats and impacts. Provenance collection is often incorporated into the reference monitor of systems to ensure that an audit trail exists of all events, that events are completely captured, and that logging of such events cannot be bypasse

Ramin Golestanian

The stochastic dynamics of tracers arising from hydrodynamic fluctuations in a driven electrolyte is studied using a self-consistent field-theory framework in all dimensions. A plethora of scaling behaviour that includes two distinct regimes of anomalous diffusion is found, and the crossovers between them are characterized in terms of the different tuning pa

Andrii Kurkin, Kevin Shen, Susanne Pielawa, Hao Wang

The instantaneous quantum polynomial (IQP) quantum circuit Born machine (QCBM) has been proposed as a promising quantum generative model over bitstrings. Recent works have shown that the training of IQP-QCBM is classically tractable w.r.t. the so-called Gaussian kernel maximum mean discrepancy (MMD) loss function, while maintaining the potential of a quantum

Jhen Hsieh, Kuan-Hsun Tu, Kuo-Han Hung, Tsung-Wei Ke

We present DexMan, an automated framework that converts human visual demonstrations into bimanual dexterous manipulation skills for humanoid robots in simulation. Operating directly on third-person videos of humans manipulating rigid objects, DexMan eliminates the need for camera calibration, depth sensors, scanned 3D object assets, or ground-truth hand and

Louis Chambard, Alrik Durand, Julien Voisin, Maxime Perdriat

We perform sensitive nuclear magnetic resonance (NMR) with spin ensembles which are polarized by nitrogen vacancy centers (NV centers) in diamond at room-temperature. With a near shot-noise-limited photoluminescence detection and a highly uniform magnetic field, we resolve sharp NMR features arising from multiple spin clusters. In particular, we investigate

Alvan Arulandu, Ilias Diakonikolas, Daniel Kane, Jerry Li

We study the complexity of two closely related learning problems, one quantum and one classical. In the quantum setting, we consider agnostic tomography for the natural class of product mixed states. Given $N$ copies of an $n$-qubit state $\rho$, the goal is to output a nearly optimal product mixed state approximation in trace distance. While recent work has

Andreas Bluhm, Marius Lemm, Tim Möbus, Oliver Siebert

The first-principles formulation of quantum mechanics relevant for quantum chemistry and trapped quantum gases involves particles in the continuous space $\mathbb R^d$. We present a unified framework and modular algorithm for learning external potentials $V$ with free-fermion models in the continuum. Compared to the lattice-based approaches, the continuum pr

Bianca-Mihaela Ganescu, Suchir Salhan, Andrew Caines, Paula Buttery

Training vision-language models on cognitively-plausible amounts of data requires rethinking how models integrate multimodal information. Within the constraints of the Vision track for the BabyLM Challenge 2025, we propose a lightweight decoder-based architecture with (1) token-wise dynamic gating for adaptive fusion of linguistic and visual cues, (2) featur

Neer Patel, Anish Giri, Hrushikesh Pramod Patil, Noah Siekierski

We present a platform-agnostic modular architecture that addresses the increasingly fragmented landscape of quantum computing benchmarking by decoupling problem generation, circuit execution, and results analysis into independent, interoperable components. Supporting over 20 benchmark variants ranging from simple algorithmic tests like Bernstein-Vazirani to

Yang Xu, Xingxing He, Shuwei Chen, Jun Liu

Automated deduction seeks to enable machines to reason with mathematical precision and logical completeness. Classical resolution-based systems, such as Prover9, E, and Vampire, rely on binary inference, which inherently limits multi-clause synergy during proof search. The Contradiction Separation Extension (CSE) framework, introduced by Xu et al. (2018), ov

Jayant Rao, Jens Eisert, Tommaso Guaita

Quantum simulation is a central application of near-term quantum devices, pursued in both analog and digital architectures. A key challenge for both paradigms is the effect of imperfections and noise on predictive power. In this work, we present a rigorous and physically transparent comparison of the stability of digital and analog quantum simulators under a

Ying Wang, Mengye Ren, Andrew Gordon Wilson

We propose In-Context Clustering (ICC), a flexible LLM-based procedure for clustering data from diverse distributions. Unlike traditional clustering algorithms constrained by predefined similarity measures, ICC flexibly captures complex relationships among inputs through an attention mechanism. We show that pretrained LLMs exhibit impressive zero-shot cluste

Chih-Yu Chang, Ming-Chung Chang

Estimating how individual input variables affect the output of a black-box model is a central task in explainable machine learning. However, existing methods suffer from two key limitations: sensitivity to out-of-distribution (OOD) evaluations, which arises when query points are placed far from the data manifold, and instability under feature correlation, wh

Jason Jabbour, Dong-Ki Kim, Max Smith, Jay Patrikar

Vision-Language-Action (VLA) models have advanced robotic capabilities but remain challenging to deploy on resource-limited hardware. Pruning has enabled efficient compression of large language models (LLMs), yet it is largely understudied in robotics. Surprisingly, we observe that pruning VLA models leads to drastic degradation and increased safety violatio

Nathaniel Johnston, Chi-Kwong Li

Given a positive integer k, it is natural to ask for a formula for the distance between a given density matrix (i.e., mixed quantum state) and the set of density matrices of rank at most k. This problem has already been solved when "distance" is measured in the trace or Frobenius norm. We solve it for all other unitary similarity invariant norms. We also pre

Songtao Jiang, Yuan Wang, Sibo Song, Tianxiang Hu

Real-world clinical decision-making requires integrating heterogeneous data, including medical text, 2D images, 3D volumes, and videos, while existing AI systems fail to unify all these signals, limiting their utility. In this paper, we introduce Hulu-Med, a transparent, generalist medical Vision-Language Model (VLM) designed to unify language-only, 2D/3D vi

David Layden, Ryan Sweke, Vojtěch Havlíček, Anirban Chowdhury

Flow models are a cornerstone of modern machine learning. They are generative models that progressively transform probability distributions according to learned dynamics. Specifically, they learn a continuous-time Markov process that efficiently maps samples from a simple source distribution into samples from a complex target distribution. We show that these

Astrid Herremans, Ben Adcock

We introduce a refinement-based Christoffel sampling (RCS) algorithm for least squares approximation in the span of a given, generally non-orthogonal set of functions $\Phi_n = \{\phi_1, \dots, \phi_n\}$. A standard sampling strategy for this problem is Christoffel sampling, which achieves near-best approximations in probability using only $\mathcal{O}(n \lo

Elisa Leonardelli, Silvia Casola, Siyao Peng, Giulia Rizzi

Many researchers have reached the conclusion that AI models should be trained to be aware of the possibility of variation and disagreement in human judgments, and evaluated as per their ability to recognize such variation. The LEWIDI series of shared tasks on Learning With Disagreements was established to promote this approach to training and evaluating AI m

M. S. Oliveira, F. A. Brito, J. A. V. Campos

In the present work, we study a subclass of Horndeski gravity characterized by a non-minimal derivative coupling between a scalar field and the Einstein tensor, as a possible alternative to alleviate the observational tension associated with estimates of the Hubble constant $H_{0}$. Two scenarios within a flat FRW spacetime were considered. In the first case

Kwanseok Kim, Jaehoon Hahm, Sumin Kim, Jinhwan Sul

Video summarization is a task of shortening a video by choosing a subset of frames while preserving its essential moments. Despite the innate subjectivity of the task, previous works have deterministically regressed to an averaged frame score over multiple raters, ignoring the inherent subjectivity of what constitutes a good summary. We propose a novel probl

Shuang Chen, Yue Guo, Yimeng Ye, Shijue Huang

Recent advances in multimodal large reasoning models (MLRMs) have substantially improved their ability to solve complex textual and visual tasks. However, these models tend to overthink on simple problems, producing unnecessarily lengthy reasoning traces, while under-exploring on challenging ones, leading to missed solutions. To address this imbalance, we pr

Ankur Mali, Lawrence Hall, Jake Williams, Gordon Richards

Activation functions govern the expressivity and stability of neural networks, yet existing comparisons remain largely heuristic. We propose a rigorous framework for their classification via a nine-dimensional integral signature S_sigma(phi), combining Gaussian propagation statistics (m1, g1, g2, m2, eta), asymptotic slopes (alpha_plus, alpha_minus), and reg

Jan-Torge Schindler, Joseph F. Hennawi, Frederick B. Davies, Sarah E. I. Bosman

We present JWST observations of the environments surrounding two high-redshift quasars, J0252$-$0503 at $z = 7.0$ and J1007$+$2115 at $z = 7.5$, which enable the first constraints on quasar-galaxy clustering at $z \sim 7.3$. Galaxies in the vicinity of the quasars are selected through ground-based and JWST/NIRCam imaging and then spectroscopically confirmed

Paul Caucal, Edmond Iancu, Farid Salazar, Feng Yuan

We revisit the calculation of the next-to-leading order (NLO) corrections to dijet production in electron-ion collisions at small $x$. We focus on the back-to-back configuration where the relative transverse momentum $P_\perp$ of the measured jets is much larger than both their momentum imbalance $K_\perp$ and the target saturation momentum $Q_s(x,A)$. In th

Kiri Sakahara, Takashi Sato

This paper argues that the finite horizon paradox, where game theory contradicts intuition, stems from the limitations of standard number systems in modelling the cognitive perception of infinity. To address this issue, we propose a new framework based on Alternative Set Theory (AST). This framework represents different cognitive perspectives on a long histo

Jon Nelson, Joel Rajakumar, Michael J. Gullans

We show that all Clifford circuits under interspersed depolarizing noise lose memory of their input exponentially quickly, even when given access to a constant supply of fresh qubits in arbitrary states. This is somewhat surprising given the result of Aharonov et al. [STOC1997] which gives a fault-tolerant protocol for general quantum circuits using a supply

Hugh Blayney, Álvaro Arroyo, Xiaowen Dong, Michael M. Bronstein

Graph Neural Networks (GNNs) leverage the graph structure to transmit information between nodes, typically through the message-passing mechanism. While these models have found a wide variety of applications, they are known to suffer from over-squashing, where information from a large receptive field of node representations is collapsed into a single fixed si

Noor Islam S. Mohammad

This study introduces a modular framework for spatial image processing, integrating grayscale quantization, color and brightness enhancement, image sharpening, bidirectional transformation pipelines, and geometric feature extraction. A stepwise intensity transformation quantizes grayscale images into eight discrete levels, producing a posterization effect th

Liang Mao, Laura Cui, Thomas Schuster, Hsin-Yuan Huang

Random unitaries sampled from the Haar measure serve as fundamental models for generic quantum many-body dynamics. Under standard cryptographic assumptions, recent works have constructed polynomial-size quantum circuits that are computationally indistinguishable from Haar-random unitaries, establishing the concept of pseudorandom unitaries (PRUs). While PRUs

Wenxuan Wang, Kai Wu, Yujian Betterest Li, Dan Wang

Foundation models for time series analysis (TSA) have attracted significant attention. However, challenges such as training data scarcity and imbalance continue to hinder their development. Inspired by complex dynamic system theories, we design a series-symbol data generation mechanism, enabling the unrestricted creation of high-quality time series data pair

Rafael Ceja Ayala, Isaac Harris, Andreas Kleefeld

In this paper, we provide an analytical study of the transmission eigenvalue problem in the context of biharmonic scattering with a penetrable obstacle. We will assume that the underlying physical model is given by an infinite elastic two--dimensional Kirchhoff--Love plate in $\mathbb{R}^2$, where the plate's thickness is small relative to the wavelength of

Øyvind Stormark Auestad, Geir-Arne Fuglstad, Annika Lang

We propose and analyse a new type of fully discrete surface finite element approximation of a class of linear parabolic stochastic evolution equations with additive noise. Our discretization uses a surface finite element approximation of the noise, and is tailored for equations with noise having covariance operator defined by (negative powers of) elliptic op

Andrew Lee, Ian Chuang, Dechen Gao, Kai Fukazawa

Visual Reinforcement Learning (RL) agents must learn to act based on high-dimensional image data where only a small fraction of the pixels is task-relevant. This forces agents to waste exploration and computational resources on irrelevant features, leading to sample-inefficient and unstable learning. To address this, inspired by human visual foveation, we in

Haider Abidi, Vitaliy Fadeyev, Tim Jones, Akhil Kumar

For the construction of the ATLAS Inner Tracker strip detector, silicon strip sensor modules are glued directly onto carbon fibre support structures using a soft silicone gel. During tests at temperatures below \unit[-35]{$^{\circ}$C}, several of the sensors were found to crack due to a mismatch in coefficients of thermal expansion between polyimide circuit

F. P. Gustafsson, M. Volponi, J. Zielinski, A. Asare

We report a proof-of-principle study demonstrating the first capture and time-of-flight spectrometry of highly charged ions (HCIs) produced following antiproton annihilations in a Penning-Malmberg trap. A multi-step nested-trap technique was developed using the \aegis\ experiment to identify annihilation-linked captured ions. The trapping and spectrometry of

Cheng Qian, Zuxin Liu, Shirley Kokane, Akshara Prabhakar

Modern LLM deployments confront a widening cost-performance spectrum: premium models deliver strong reasoning but are expensive, while lightweight models are economical yet brittle on complex tasks. Static escalation rules and keyword heuristics under-utilize this spectrum and fail to adapt across task types. We present xRouter, a tool-calling-based routing

Xi Fang, Bingkai Wang, Liangyuan Hu, Fan Li

Cluster-randomized trials (CRTs) are experimental designs where groups or clusters of participants, rather than the individual participants themselves, are randomized to intervention groups. Analyzing CRT requires distinguishing between treatment effects at the cluster level and the individual level, which requires a clear definition of the estimands under t

Kevin J. Kelly

Neutrino oscillations are a phenomenon that has been observed for over two decades and leads to the conclusion that neutrinos have mass. The Standard Model predicts massless neutrinos, and so neutrinos require physics beyond the Standard Model. Other signatures of BSM physics are detectable in modern neutrino facilities -- this chapter explores those possibi

Rui Zhao, Zihan Chen, Zemin Zheng

High-dimensional linear contextual bandit problems remain a significant challenge due to the curse of dimensionality. Existing methods typically consider either the model parameters to be sparse or the eigenvalues of context covariance matrices to be (approximately) sparse, lacking general applicability due to the rigidity of conventional reward estimators.

Laura Cui, Thomas Schuster, Liang Mao, Hsin-Yuan Huang

The nature of randomness and complexity growth in systems governed by unitary dynamics is a fundamental question in quantum many-body physics. This problem has motivated the study of models such as local random circuits and their convergence to Haar-random unitaries in the long-time limit. However, these models do not correspond to any family of physical tim

E. C. Valadão, Felipe Sobrero, Santiago Esteban Perez Bergliaffa

The Lyra geometry provides an interesting approach to develop purely geometrical scalar-tensor theories. Here we present a theory on Lyra manifolds which contains generalizations of both Brans-Dicke gravity and Einstein-Gauss-Bonnet scalar-tensor theory. It is shown that the symmetry group of gravitational theories on the Lyra geometry comprises not only coo

Kaiwen Zheng, Yuji Wang, Qianli Ma, Huayu Chen

Although continuous-time consistency models (e.g., sCM, MeanFlow) are theoretically principled and empirically powerful for fast academic-scale diffusion, its applicability to large-scale text-to-image and video tasks remains unclear due to infrastructure challenges in Jacobian-vector product (JVP) computation and the limitations of evaluation benchmarks lik

Ahmedeo Shokry, Alessandro Santini, Filippo Vicentini

The natural gradient is central in neural quantum states optimizations but it is limited by the cost of computing and inverting the quantum geometric tensor, the quantum analogue of the Fisher information matrix. We introduce a block-diagonal quantum geometric tensor that partitions the metric by network layers, analogous to block-structured Fisher methods s

Stella C. Dong, James R. Finlay

Reinsurance treaty pricing must satisfy stringent regulatory standards, yet current quoting practices remain opaque and difficult to audit. We introduce ClauseLens, a clause-grounded reinforcement learning framework that produces transparent, regulation-compliant, and risk-aware treaty quotes. ClauseLens models the quoting task as a Risk-Aware Constrained Ma

Rafael S. Dutra, Felipe A. Pinheiro, Diney S. Ether, Cyriaque Genet

We demonstrate that an effect phenomenologically analogous to circular dichroism can arise even for dielectric and isotropic chiral spherical particles. By analyzing the polarimetry of light scattered from a chiral, lossless microsphere illuminated with linearly polarized light, we show that the scattered light becomes nearly circularly polarized, exhibiting

Sujit Rao

We give a convergent hierarchy of SDP certificates for bounding the spectral gap of local qubit Hamiltonians from below. Our approach is based on the NPA hierarchy applied to a polynomially-sized system of constraints defining the universal enveloping algebra of the Lie algebra $\mathfrak{su}(2^{n})$, as well as additional constraints which put restrictions

Zhengtian Qiu, Shouhong Qiao

Let $ H $ be a subgroup of a finite group $ G $. We say that $ H $ satisfies the $ \Pi $-property in $ G $ if $ | G/K : N_{G/K}((H \cap L)K/K)| $ is a $ \pi(( H \cap L)K/K ) $-number for any chief factor $ L/K $ of $ G $; and we call that $ H $ satisfies the $ IC $-$ \Pi $-property in $ G $ if $ H\cap [H, G] $ satisfies the $ \Pi $-property in $ G $. In this

Yihong Luo, Tianyang Hu, Jing Tang

While reinforcement learning methods such as Group Relative Preference Optimization (GRPO) have significantly enhanced Large Language Models, adapting them to diffusion models remains challenging. In particular, GRPO demands a stochastic policy, yet the most cost-effective diffusion samplers are based on deterministic ODEs. Recent work addresses this issue b

Adriana Valio, Eder Martioli, Andre O. Kovacs, Viktor Y. D. Sumida

Measuring starspot temperatures is crucial for understanding stellar magnetic activity, as it affects stellar brightness variations, influences exoplanet transit measurements, and provides constraints on the physical conditions and energy transport in active regions, offering insights into stellar dynamos. Our goal is to determine the temperature of starspot

Ilya Merkulov, Rotem Arnon

We introduce a systematic approach for analyzing device-independent single-prover interactive protocols under computational assumptions. This is done by establishing an explicit correspondence with Bell inequalities and nonlocal games and constructing a computational space of correlations. We show how computational assumptions are converted to computational

Glen P. Kiely, Bruno Semião, Evgeniia Ponomareva, Rafael Nuñez-Palacio

The energy losses of energetic ions in materials depend on both nuclear and electronic interactions. In channeling geometries, the stopping effect of these interactions can be highly reduced, resulting in deeper ion penetration. Comprehensive, trajectory-dependent models for ion-material interactions are therefore crucial for the accurate prediction of ion r

Kaustav Mitra, Frank C. van den Bosch

The Conditional Luminosity Function (CLF) is an effective and flexible way of characterizing the galaxy-halo connection. However, it is subject to a particular choice for its parametrization, which acts as a prior assumption. Most studies have been restricted to what has become a standard CLF parametrization with little to no variation. The goal of this pape

Maite Arcos, Renato Renner, Jonathan Oppenheim

Betting games provide a natural setting to capture how information yields strategic advantage. The Kelly criterion for betting, long a cornerstone of portfolio theory and information theory, admits an interpretation in the limit of infinitely many repeated bets. We extend Kelly's seminal result into the single-shot and finite-betting regimes, recasting it as

Mariano Casas-Luna, Dalibor Preisler, Jiří Kozlík, Josef Stráský

This study explores Ti-containing complex concentrated alloys (CCAs) within the AlMoNbTiZr system, focusing on compositions located in regions of the Bo-Md diagram characterized by low bond order (Bo) and d-orbital energy level (Md). Four alloys were designed near the line predicting stress-induced martensite formation in conventional Ti alloys, then cast, a

Filippos Dakis, Shruti Puri, Sophia E. Economou, Edwin Barnes

Erasure qubits -- qubits designed to have an error profile that is dominated by detectable leakage errors -- are a promising way to cut down the resources needed for quantum error correction. There have been several recent experiments demonstrating erasure qubits in superconducting quantum processors, most notably the dual-rail qubit defined by the one-photo

Leonardo N. Ferreira, Haroon Mumtaz, Ana Skoblar

This paper introduces a Bayesian vector autoregression (BVAR) with stochastic volatility-in-mean and time-varying skewness. Unlike previous approaches, the proposed model allows both volatility and skewness to directly affect macroeconomic variables. We provide a Gibbs sampling algorithm for posterior inference and apply the model to quarterly data for the U

Mireille Bousquet-Mélou, Hadrien Notarantonio

We consider the $3$-state Potts generating function $T(\nu,w)$ of planar triangulations; that is, the bivariate series that counts planar triangulations with vertices coloured in $3$ colours, weighted by their size (number of vertices, recorded by the variable $w$) and by the number of monochromatic edges (variable $\nu$). This series was proved to be algebr

Mohammad Baqar

Modern software teams frequently encounter delays in resolving recurring or related issues due to fragmented knowledge scattered across JIRA tickets, developer discussions, and GitHub pull requests (PRs). To address this challenge, we propose a Retrieval-Augmented Generation (RAG) framework that integrates Sentence-Transformers for semantic embeddings with F

David Madras, Joshua Safyan, Qiuyi, Zhang

Many prompt engineering techniques have been successful in practice, even when optimizing over a large prompt space with with a small amount of task-specific data. Recent work has partially explained this success by showing generalization bounds which apply PAC-Bayes theory to the discrete prompt space, but they are non-vacuous only in data-rich scenarios. W

Natalia Mrnjavac, Nadja K. Hoffmann, Manon L. Schlikker, Maximilian Burmeister

The origin of life required the emergence of metabolism, an autocatalytic network of enzymatic reactions that synthesize amino acids, nucleotides and cofactors. At the origin of metabolism there were no enzymes--how did it start? Empirical studies addressing early metabolic evolution are lacking. Harnessing protein structures for metabolic enzymes, we identi

Yu-Han Wu, Quentin Berthet, Gérard Biau, Claire Boyer

We identify and analyze a surprising phenomenon of Latent Diffusion Models (LDMs) where the final steps of the diffusion can degrade sample quality. In contrast to conventional arguments that justify early stopping for numerical stability, this phenomenon is intrinsic to the dimensionality reduction in LDMs. We provide a principled explanation by analyzing t

Bibekananda Patra, Rajeevlochana G. Chittawadigi, Sandipan Bandyopadhyay

This paper presents a method of validation of the size of the largest collision-free sphere (CFS) of a 6-6 Stewart-Gough platform manipulator (SGPM) for a given orientation of its moving platform (MP) using the Application Programming Interface (API) of a CAD software. The position of the MP is updated via the API in an automated manner over a set of samples

Lauren Anderson, Lucas Chatelain, Nicolas Tremblay, Kathryn Grandfield

The mechanosensory system of teeth is currently believed to partly rely on Odontoblast cells stimulation by fluid flow through a porosity network extending through dentin. Visualizing the smallest sub-microscopic porosity vessels therefore requires the highest achievable resolution from confocal fluorescence microscopy, the current gold standard. This consid

Filip Bečanović, Kosta Jovanović, Vincent Bonnet

Inverse optimal control (IOC) allows the retrieval of optimal cost function weights, or behavioral parameters, from human motion. The literature on IOC uses methods that are either based on a slow bilevel process or a fast but noise-sensitive minimization of optimality condition violation. Assuming equality-constrained optimal control models of human motion,

Gereon Koßmann, Mario Berta, René Schwonnek

Device-independent quantum key distribution (DIQKD) promises cryptographic security based solely on observed quantum correlations, yet its implementation over long distances remains limited. Routed Bell tests have recently re-emerged as a promising strategy to mitigate this limitation by enabling local self-testing of one party's device. However, extending t

Noor Ul Zain, Mohsin Raza, Ahsan Adeel

We show that a tiny Co$^4$ machine(Adeel,2025) with a single layer, two heads, and 8M parameters, operating at an approximate cost of $O(N)$ (where $N$ is the number of input tokens), outpaces the BabyLM Challenge baselines GPT-2 (124M, 12 layers, $O(N^2))$ and GPT-BERT (30M, 12 layers, $O(N^2))$ in just two epochs, while both are trained for ten. Co$^4$ ach

Stacey Jeffery, Galina Pass

Directed $st$-connectivity (DSTCON) is the problem of deciding if there exists a directed path between a pair of distinguished vertices $s$ and $t$ in an input directed graph. This problem appears in many algorithmic applications, and is also a fundamental problem in complexity theory, due to its ${\sf NL}$-completeness. We show that for any $S\geq \log^2(n)

Benjamin Anker, Milad Marvian

We propose a scheme for the fault-tolerant implementation of arbitrary Clifford circuits. To achieve this, we extend previous work on flag gadgets for syndrome extraction to a general framework that flags any Clifford circuit. This framework opens new pathways toward universal fault tolerance by allowing transversal implementation of $T$ gates alongside faul

Ekaterina A. Ilicheva, Irina A. Chuyko, Lev O. Luchnikov, Polina K. Sukhorukova

Despite competitive efficiency compared to Si solar cells and relevant stability at near room temperatures the rapid degradation at elevated temperatures remains the critical obstacle for exploitation of perovskite photovoltaics. In this work, a 4-(pyridin-4-yl)triphenylamine (TPA-Py) with pyridine anchor group was employed for inter-grain bulk modification

Yuxin Ma, Lun Du, Lanning Wei, Kun Chen

Diffusion-based large language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs, leveraging denoising-based generation to enable inherent parallelism. Even more and more open-sourced dLLM models emerge, yet their widespread adoption remains constrained by the lack of a standardized and efficient inference framework. We prese

James Bartusek, Aparna Gupte, Saachi Mutreja, Omri Shmueli

A classical obfuscator for quantum circuits is a classical program that, given the classical description of a quantum circuit $Q$, outputs the classical description of a functionally equivalent quantum circuit $\hat{Q}$ that hides as much as possible about $Q$. Previously, the only known feasibility result for classical obfuscation of quantum circuits (Bartu

Aofan Liu, Lulu Tang

Vision-Language Models (VLMs) have garnered significant attention for their remarkable ability to interpret and generate multimodal content. However, securing these models against jailbreak attacks continues to be a substantial challenge. Unlike text-only models, VLMs integrate additional modalities, introducing novel vulnerabilities such as image hijacking,

Jinhua Hong, Alberto Guandalini, Weibin Wu, Haiming Sun

Excitons govern the light-matter interaction in 2D gapped materials with intrinsically large binding energies. In spite of plentiful optical measurements in the visible for semiconducting transition-metal dichalcogenides, we still lack optical-absorption studies of the exciton structure of insulating 2D materials that requires UV light. Moreover, measurement

Heming Zou, Yunliang Zang, Wutong Xu, Yao Zhu

Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning method for foundation models, but it suffers from parameter interference, resulting in suboptimal performance. Although Mixture-of-Experts (MoE)-based LoRA variants show promise in mitigating intra-task correlations in single-task instruction tuning, they introduce additional router

A. R. Goswami

Let $G(V,E)$ be a graph, and $\mathscr{H}:=\big\{H:H\subseteq G\big\}$ denote the collection of all possible subgraphs of $G$. Then for each non-negative function $w:\mathscr{H}\to\mathbb{R_+}$, the graph $G(V,E,w)$ is said to be a weighted graph. A weighted graph $G(V,E,w)$ is called monotone (increasing), if for any $H_1,H_2\subseteq G$ with $H_1\subset H_

Shoeb Joarder, Mohamed Amine Chatti

The aim of learning analytics is to turn educational data into insights, decisions, and actions to improve learning and teaching. The reasoning of the provided insights, decisions, and actions is often not transparent to the end-user, and this can lead to trust and acceptance issues when interventions, feedback, and recommendations fail. In this paper, we sh

Mustafa B. Yaldiz, Ishit Mehta, Nithin Raghavan, Andreas Meuleman

Neural fields excel at representing continuous visual signals but typically operate at a single, fixed resolution. We present a simple yet powerful method to optimize neural fields that can be prefiltered in a single forward pass. Key innovations and features include: (1) We perform convolutional filtering in the input domain by analytically scaling Fourier

Ziqi Zhang, Yuexiang Li, Yawen Huang, Nanjun He

Recent studies have uncovered a new research line, namely source-free domain adaptation, which adapts a model to target domains without using the source data. Such a setting can address the concerns on data privacy and security issues of medical images. However, current source-free domain adaptation frameworks mainly focus on the pseudo label refinement for

Guobin Ma, Jixun Yao, Ziqian Ning, Yuepeng Jiang

Zero-shot voice conversion (VC) aims to transfer timbre from a source speaker to any unseen target speaker while preserving linguistic content. Growing application scenarios demand models with streaming inference capabilities. This has created a pressing need for models that are simultaneously fast, lightweight, and high-fidelity. However, existing streaming

Yue Yu, Myung-Joong Hwang

The spontaneous breaking of a $Z_2$ symmetry typically gives rise to emergent excitations possessing the same symmetry with a renormalized mass. Contrary to this conventional wisdom, we present a theory in which the low-lying excitation in the broken-symmetry phase acquires a continuous symmetry, even when the underlying symmetry of the system is discrete. I

Shunhong Deng, Zhihong He, Anbing Ren, Qian Cui

Classical Cepheids (CCs) in Galactic open clusters (OCs) provide essential observational constraints for calibrating the period-age relation (PAR) and the period-Wesenheit relation (PWR) of CCs. However, distant and long-period OC Cepheids remain limited, while the confirmed samples still require more precise determinations of their physical properties, such

Rui Wang, Zeming Wei, Guanzhang Yue, Meng Sun

Detecting hallucinations in large language models (LLMs) remains a fundamental challenge for their trustworthy deployment. Going beyond basic uncertainty-driven hallucination detection frameworks, we propose a simple yet powerful method that quantifies uncertainty by measuring the effective rank of hidden states derived from multiple model outputs and differ

Jasmin Orth, Philipp Mondorf, Barbara Plank

Conditional acceptability refers to how plausible a conditional statement is perceived to be. It plays an important role in communication and reasoning, as it influences how individuals interpret implications, assess arguments, and make decisions based on hypothetical scenarios. When humans evaluate how acceptable a conditional "If A, then B" is, their judgm

James Freitag

We show that if any four distinct solutions of a rational difference equation are algebraically independent, then any number of distinct solutions to the equation are independent. A nontrivial variant of this result is given for autonomous difference equations or algebraic dynamical systems, where we show the degree of nonminimality is at most one. The resul

Sourav Das, Aiman Khan, Francesco Albarelli, Animesh Datta

We provide the ultimate precision attainable in spectroscopy of a quantum emitter using single-photon pulses. We find the maximum for estimating the linewidth to be independent of the details of the emitter's bare Hamiltonian while that for the detunings not to be so. We also identify optimal pulse shapes attaining these precisions.

Sofia Kirsanova, Yao-Yi Chiang, Weiwei Duan

Historical map legends are critical for interpreting cartographic symbols. However, their inconsistent layouts and unstructured formats make automatic extraction challenging. Prior work focuses primarily on segmentation or general optical character recognition (OCR), with few methods effectively matching legend symbols to their corresponding descriptions in

Miriam Kuzbary, Shashank G. Markande, Elisabetta A. Matsumoto, Stanley Pritchard

In this study, we use a correspondence between two-periodic weft-knitted textiles and links in the thickened torus to study the former using link invariants. We establish a criterion to identify the set of links whose elements are realized through techniques of weft-knitting leading to new, unconventional types of weft-knitting stitch patterns. A crucial top

Yi Jiang, Lei Shen, Lujie Niu, Sendong Zhao

Large language models (LLMs) excel at natural language tasks but are limited by their static parametric knowledge, especially in knowledge-intensive task. Retrieval-augmented generation (RAG) mitigates this by integrating external information. However, (1) traditional RAG struggles with complex query understanding, and (2) even search agents trained with rei

Jacob Trauger, Tyson Trauger, Ambuj Tewari

In this paper we will give a characterization of the learnability of forgiving 0-1 loss functions in the multiclass setting with effectively finite cardinality of the output and label space. To do this, we create a new combinatorial dimension that is based off of the Natarajan Dimension and we show that a hypothesis class is learnable in our setting if and o

Baoyang Chen, Xian Xu, Huamin Qu

As industrial robots move into shared human spaces, their opaque decision making threatens safety, trust, and public oversight. This artwork, Airy, asks whether complex multi agent AI can become intuitively understandable by staging a competition between two reinforcement trained robot arms that snap a bedsheet skyward. Building on three design principles, c

DUNE Collaboration, S. Abbaslu, F. Abd Alrahman, A. Abed Abud

The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment with a rich physics program that includes searches for the hypothetical phenomenon of proton decay. Utilizing liquid-argon time-projection chamber technology, DUNE is expected to achieve world-leading sensitivity in the proton decay channels that involve charged kaons in

Anu Tripathi, Alison Brooks, Traci Snedden, Peter Ferrazzano

Purpose: Computational head models are essential tools for studying the risk of mild traumatic brain injury (mTBI) under different activities and across populations. However, different computational models incorporate varied levels of anatomical details, such as cortical folds. In this study, we aim to determine the effect of modeling cortical folds on mTBI

Jesse Beisegel, Katharina Klost, Kristin Knorr, Fabienne Ratajczak

We consider the problem of finding a Hamiltonian path or cycle with precedence constraints in the form of a partial order on the vertex set. We study the complexity for graph width parameters for which the ordinary problems $\mathsf{Hamiltonian\ Path}$ and $\mathsf{Hamiltonian\ Cycle}$ are in $\mathsf{FPT}$. In particular, we focus on parameters that describ

Carlos Eduardo Estanislau, Thiago Colla, Christos N. Likos

Attractive dipole interactions can be induced between equally charged soft nanoparticles under the influence of AC electric fields. The combination of charge repulsion and dipole attraction, along with different screening responses from an underlying electrolyte, lead to complex aggregations ranging from chain-like formation for linear polarizations to isotr