October 2026 arXiv papers — page 2

Gaetano Agazzotti, Antonin Chambolle, Clément W. Royer

The network simplex method is a classical algorithm for minimum cost flow problems formulated as linear programs, that has attracted recent interest from the optimal transport community. Indeed, discrete optimal transport is a subclass of minimum cost flow for which network simplex techniques compute exact solutions while leveraging the inherent problem spar

Roman Drnovšek, Damjana Kokol Bukovšek, Nik Stopar

Given a bivariate copula $C$, the bivariate copula $Ψ(C)$ is defined as the Markov product of $C^T$ and $C$. In this paper we consider the diagonal section of $Ψ(C)$ under the additional assumption that its value at a single point is known. We determine the exact upper and lower bounds for the diagonal section of $Ψ(C)$, given that $Ψ(C)(a,a)=d \in [a^2,a]$,

Robert J. Banks, Natasha Feinstein, Roopayan Ghosh, Sebastian Schulz

The introduction of two-body XX interactions has been shown to improve the performance of quantum annealing in solving certain combinatorial optimisation problems and would enable the simulation of a broader class of many-body Hamiltonians. Due to experimental constraints, such interactions are typically not natively available on analogue quantum devices. To

Mason Minot, Simon Friedensohn, Cédric R. Weber

Generative molecular design typically relies on over-generation and post-hoc filtering. Inference-time steering methods like Feynman-Kac (FK) steering improve efficiency by biasing trajectories toward high-reward candidates, yet they optimize within a single fixed configuration. In de novo nanobody design, upfront commitment to conditioning choices like scaf

Masoud Hakimi Heris, Yuan Liu, Frank Mueller

Scaling trapped-ion processors by adding ions remains challenging. A complementary route exploits the multilevel internal structure of each ion to increase the computational Hilbert space per ion. We introduce a hardware-native hybrid qudit-oscillator instruction set for a general d-level internal register coupled to bosonic modes and prove that three of its

Luka Paljk, Johannes Schmidt, Vladislav Popkov

We find a robust scenario for the emergence of the Golden Ratio (GR) dynamical universality class, characterized by the dynamical exponent $z \equiv φ=\frac{1}{2}(1+\sqrt{5})$. Previously, its emergence required fine-tuning of system parameters. Here, the GR universality class appears generically in a wide class of Markov processes as a consequence of a glob

Daniel Thi Graviet, Tanmay Shah

Coding agents often make useful progress before they fail, but retry-from-scratch discards the machine state created along the way. This makes failure recovery an interesting search problem: if intermediate environment states preserve meaningful progress, restoring and branching from them could outperform repeatedly restarting from the initial state. We impl

Emilio Ferrara

Comics are one of the great visual records of the twentieth century: hundreds of thousands of pages in which a mass readership saw its leaders, its wars and its heroes. Most of this record has never been studied as a whole; comics scholarship proceeds one book at a time. We argue that artificial intelligence, including generative models, can change this to t

Guillermo Romero Moreno, Valentin Robu, Han La Poutré

Disinformation on online social media has been on the rise in the past years. While there is extensive literature modelling social cascades, online disinformation cascades show distinctive characteristics: they provoke debunking reactions, they spread over networks with heterogeneous connectivity, and they compete for the limited attention of users --- which

Valentýn Číhala, Pavel Sarga, Martin Pecka, Tomáš Svoboda

Nearly every autonomous or semi-autonomous flipper controller assumes access to an elevation map of the terrain under and in front of the robot. The terrain beneath the chassis is occluded in the sensors, so the map is remembered through odometry integration, whose drift corrupts it. We present Stabilizing Anti-Stuck (SAS), a mapless controller that combines

Xiao-Yu Qi, Qi Wu, Dian-Yong Chen

In the present work, we investigate the long-distance contributions to light hadrons and radiative decays of $η_c$, where the long-distance contribution is phenomenologically described by the meson loops. Our estimations indicate that the branching fractions of $η_c \to \mathcal{VV},\ \mathcal{V}=(ρ,ω, ϕ)$ can be roughly reproduced in the range $1.4<α<1.6$.

Egor Kosov

We prove a dimension-free Markov--Bernstein-type inequality for polynomial mappings $(f_1,f_2)\colon\mathbb R^n\to\mathbb R^2$ of degree at most $d$. For product probability measures whose one-dimensional marginals have densities bounded by $1$, we obtain a lower bound for the integral of the Gram determinant of $\nabla f_1$ and $\nabla f_2$ in terms of the

Sheng Zhao, Yuhao Zhu

An ideal (language) model must continually update its knowledge. Existing work largely focuses on making these updates efficient after training. We investigate how the organization of time-varying knowledge during pre-training shapes the efficiency of subsequent updates. Models trained on chronologically ordered knowledge specialize in the most recently enco

Oscar Davis, Daan Roos, Alice Bizeul, Amitis Shidani

Continuous diffusion language models (CDLMs) promise to bring the benefits of continuous flow modelling to language, including accelerated sampling and controllable generation. The field has grown rapidly, and the capabilities of CDLMs have begun to rival those of discrete diffusion. Yet, unlike for continuous modalities such as images or videos, our grasp o

Konstantinos Konstantos, Pavlos Motakis, Thomas Speckhofer

In 1981, Bourgain, Rosenthal, and Schechtman constructed uncountably many pairwise non-isomorphic complemented subspaces of $L_p$ for $1 < p < \infty$, $p \neq 2$. We extend their construction to rearrangement-invariant spaces and Hardy spaces. For any separable rearrangement-invariant space $X$ with non-trivial Boyd indices, we show that these subspaces are

Yuhong Liu, Shenxi Wu, Junyi Chi, Beichen Zhang

When solving complex spatial reasoning tasks, ordinary humans naturally revisit the scenes from task-relevant viewpoints and mentally reconstruct the spatial layouts from those viewpoints. But can Large Vision-Language Models (LVLMs) proactively think in the same way? We term this ability Active Spatial Mentalization (ASM), an intrinsic component of visual i

Gordon Heier, Ravi Mudaliar, Haotian Zuo

Based on earlier work by Wiegerinck and Huang-Li, Hayashida-Kamimoto constructed unbounded non-smooth Reinhardt domains in \({\mathbb C}^n\), \(n\geq 3\), whose Bergman spaces are finite-dimensional and whose Bergman metrics have constant positive holomorphic sectional curvature \(2\). In this note, we prove a Sandwich Principle for the Hayashida-Kamimoto do

Rana Ghazali, Douglas G. Down

The increasing heterogeneity and dynamism of cloud, fog, and edge computing make scheduling a challenging problem involving diverse resource capabilities, workload characteristics, latency and energy constraints, and competing optimization objectives. Although numerous scheduling methods have been proposed, existing reviews often provide fragmented coverage

Jingwei Song, Zhisheng Chen, Bingfan Zeng, Jing Tang

A central challenge in embodied recursive self-improvement is enabling agents to convert interaction history into capabilities that persist beyond one world and transfer to later tasks. A saved memory or successful final action does not by itself establish that a reusable capability was acquired; evaluation must separate capability change from carried-over r

Anibal Bezerra, Fernando G. Gardim

Experiments in relativistic heavy-ion collisions determine centrality from final-state quantities, usually the charged-particle multiplicity. Performing the same in hydrodynamic simulations requires millions of events, which is impractical for state-of-the-art hybrid models, and centrality is therefore commonly approximated by the initial entropy or energy.

Livia Grammatica

We answer a question of Chai about the rigidity of Tate-linear subvarieties of $\mathcal{A}_g$ over $\overline{\mathbb{F}}_p$: we show that that a smooth subvariety of a central leaf of $\mathcal{A}_g$ which is Tate-linear at one point is Tate-linear at every point. This is achieved by a careful analysis of the $p$-adic monodromy groups of Tate-linear variet

Fu-An Chao, Bi-Cheng Yan, Berlin Chen

Recent advances in automatic pronunciation assessment (APA) have driven a shift from scripted to free-form assessment. However, existing methods often rely on multi-stage pipelines with forced alignment and separate feature extractors, limiting their practicality across response settings. In light of this, we propose OpenPA, a novel APA framework that levera

Naman Arora, Adarsh Ganesan

Phononic frequency combs are equidistant spectral ladders generated when a driven vibrational mode transfers energy to an autoparametrically excited subharmonic mode through anharmonic intermode coupling. In previous studies, the coupling strength governing this energy transfer has typically been treated as a free parameter, extracted from a fabricated devic

Silviu S. Pufu, Benjamin T. Søgaard, Spyros Terlemes

The Relativistic Continuous Matrix Product State (RCMPS) ansatz introduced in arXiv:2102.07741 is a translation-invariant variational ansatz intended to approximate the ground states of gapped one-dimensional relativistic Quantum Field Theories directly in the continuum limit and in infinite volume. In this work, we develop an ansatz, referred to as the Rela

Oktie Hassanzadeh, Dharmashankar Subramanian

Semantic query engines ask a large language model (LLM) to judge individual rows, and benchmarks compare these engines on the quality of a few executions at temperature 0. We measure how much another execution changes. We re-execute the text and tabular workloads of SemBench, LRO-Bench, and UDA-Bench through their own harnesses, log every one of 1.2 million

Chang Liu, Hao Shen, Chuanying Li

Physical diagnostics recorded during simulation can organize a sharply varying response, but using them as surrogate inputs normally requires another calculation at prediction. We construct a physical-state-supervised Gaussian process surrogate that learns these diagnostics from design variables. Cross-fitted state estimates augment a retained design represe

Juan Terven, Diana-Margarita Córdova-Esparza, Ruben Gomez, Carlos Miranda Rocha

A useful language model should know when to answer and when to say &#34;I don&#39;t know.&#34; Can we improve that decision by training the model itself or by training a separate component to check its answers? We study both approaches on Qwen3-14B for factual questions, using a score that rewards correct answers, penalizes mistakes, and allows abstention. A

Hyunwoo Lee

An $n$-vertex graph $G$ is $(ρ, d)$-dense if for all $U \subseteq V(G)$ of size at least $ρn$, the induced subgraph $G[U]$ contains at least $\frac{d}{2}|U|^2$ edges. In 2010, partly motivated by the theory of quasirandomness, Kohayakawa, Nagle, Rödl, and Schacht conjectured that, for any fixed graph $F$, the number of homomorphic copies of $F$ in a given $n

Jose Roberto Tello Ayala, Akl C. Fahed, Roy Perlis, Finale Doshi-Velez

In clinical practice, experts often begin by converting raw inputs into a set of expert features before making a decision. While black-box models trained on raw input can outperform models trained on expert views, it remains unclear what predictive signal the raw model exploits beyond what the expert view captures. We introduce an audit framework to quantify

Rafiq Kamel, Nicolas Atienza, Aziz Fouché, Nicola Cancedda

Representations of supervised datasets support higher-level machine-learning tasks such as dataset retrieval and distribution-shift monitoring. However, existing approaches typically rely either on hand-crafted meta-features or on dataset encoders trained specifically for representation learning. We introduce TabMF, a framework that repurposes pretrained tab

Claudio Alexandre Piedade, Philippe Tranchida

Abstract polytopes and hypertopes are thin and residually-connected incidence geometries or, equivalently, chamber systems. Their degree of regularity is understood by looking at the action of their automorphism group on the set of their chambers. When this action has only one orbit, they are called regular. In this article, we are interested in polytopes an

Bahey Tharwat, Iman Nematollahi, Marvin Alles, Xingyuan Zhang

Generalist robot policies are trained with imitation learning and are bounded by the quality and coverage of their demonstrations. Reinforcement learning can improve and extend them, either by fine-tuning the policy weights or by training a small module to steer the input noise of a frozen generative policy. When tasks arrive sequentially, both approaches su

Michele Bertozzi, Lorenzo Amelio, Sebastiano Strangio, Nikita J. Greenidge

Wireless capsule endoscopy is constrained by severe limitations in size, available power, and reliable data transmission through biological tissues. Although recent advances in miniature imaging sensors have improved image quality, the wireless transmission of color images at practical frame rates remains a challenging task under the tight capsule constraint

A. A. Araújo Filho

We construct an exact stationary axisymmetric vacuum configuration in metric affine Kalb-Ramond gravity with zero cosmological constant. Independent variation of the metric, connection, and antisymmetric field yields the complete field equations and the algebraic relation between the physical and auxiliary geometries. A Kerr auxiliary metric and a closed cor

Valentino Badalucco

Standing waves $e^{iωt}u(x)$ of nonlinear dispersive equations, such as the nonlinear Schrödinger and Klein--Gordon equations, have a profile $u$ that solves a nonlinear elliptic ODE of the form $Δu+f(u)=0$. We prove the existence of radial bound states with a prescribed number of nodal surfaces for this equation, on a large class of spherically symmetric ma

Teo Deveney, Lea Bogensperger, Hok Shing Wong

Flow-matching and diffusion models are increasingly used as priors for linear inverse problems, however, most methods are bespoke to one class of trajectory prior. Plug-and-play methods have been proposed, however these implicitly decorrelate the guided trajectory, resulting in broad priors that can struggle when data is sparse. This work proposes $β$-Flow:

Meriem Terki, Nouhaila Innan, Minghao Shao, Muhammad Kashif

Large language models (LLMs) have shown strong results in classical code generation and are increasingly used to write the unit tests that safeguard that code. Whether they can do the same for quantum software is less clear, because quantum programs are probabilistic, depend on numerical tolerances, and are judged by domain-specific checkers, so a well-forme

Amit Kumar Jaiswal, Sagar Uprety, Prayag Tiwari

Large language models (LLMs) are now a standard source of query expansion terms but they hallucinate. They inject non existent entities and off-topic terms that add ranking noise and can hurt strong retrievers. Existing remedies such as corpus grounding, mutual verification and preference alignment treat hallucinated terms as an undifferentiated nuisance to

Soham Sinha, Rahul Choudhury, Shekhar Dwivedi, Gregory Lee

Real-time embedded systems increasingly place GPU-accelerated processing of high-bandwidth sensor streams inside closed-loop systems, but the CPU (the host) remains the primary orchestrator of each iteration. The host observes sensor inputs, submits CUDA work, waits for completion, and commits actuation. This host-mediated path exposes the closed-loop end-to

Jingyan Xu, Yuanhong Wu, Samuel Yen-Chi Chen, Sean Patten

Variational quantum circuits (VQCs) are highly sensitive to circuit architecture, yet most quantum machine learning pipelines select a single best design and discard the remaining candidates. This paper addresses whether the structural diversity across quantum circuit search spaces can be used to produce stronger predictions through model fusion. We apply Co

Katsumi Ibaraki, David Chiang

Data augmentation is standard practice for low-resource automatic speech recognition (ASR), but its benefits are almost always reported as aggregate error rates, leaving open why and when a method helps. On four low-resource languages and English at five data sizes, we cross two augmentations that modify existing audio (speed perturbation and SpecAugment) wi

Vishnu Arun, Adam Obeng, Tianyou Wang, Zilin Ren

Production recommender systems have feature-lifecycle asymmetry: adding new features must produce gains on some metric, while feature removal must not regress any metric. In practice, feature deprecation is hard to prioritize as a feature can fail to be removed if it interacts with other features, its importance changes daily, it cannot be evaluated before p

Babak Aslani, Mojtaba Tefagh, Gourav Jhanwar, Masoud Zarepisheh

Interior-point (IP) methods are powerful second-order optimization algorithms but can make slow progress during early iterations. We propose Warm-IP, a hybrid framework that combines a path-following variant of the alternating direction method of multipliers (ADMM) with a primal--dual IP method. The ADMM phase generates a warm start that remains close to the

Boyuan Deng, Kshitiz Upadhyay, Michael Shields

Gaussian process solvers for linear partial differential equations (PDEs) return posteriors rather than point estimates, but they condition only on equality information: the PDE residual, boundary data, and measurements. Many solutions of interest also obey inequality constraints, including nonnegativity, monotonicity, and absence of arbitrage, which equalit

Mattia Ornaghi

Let $f$, $g\colon A\to B$ be two A$_{\infty}$-morphisms between two A$_{\infty}$-algebras (linear over a commutative ring $\mathbb{K}$). In this paper, we compare different notions of (right) homotopy between $f$ and $g$. We note that the A$_{\infty}$-algebra $B\otimes R$, where $R:=\mathbb{K}[t,dt]$, is a model for the interval $[0,1]\times B$ in the sense

Kabir Khanna, Sara Murciano, Romain Vasseur

Performing measurements on gapless quantum ground states can give rise to new universal phenomena, whose origins remain poorly understood even for the paradigmatic Ising critical point. Replica methods offer a natural framework to study such post-measurement ensembles, with the replica limit $Q\to 1$ describing physical Born-averaged observables. Here we sho

Daniel T. Wise

We complete the proof that every compact 3-manifold group is linear. Specifically, we construct a faithful representation in GL5(Q[u]) for a finite-index subgroup of every closed non-geometric graph-manifold group.

Akihiro Tanaka

We study the Euler-Maruyama approximation of the degenerate stochastic differential equation $dX_t = |X_t|^α\,dW_t$, $0 < α< 1/2$, $X_0 = 0$, for which uniqueness in law fails. The Euler scheme started exactly from zero is trapped by the absorbing solution, whereas a nonzero initialization $x_h \to 0$ can select a different weak solution. We identify the ini

Wei Jiang, Yibo Wang, Zechao Li

Variance reduction improves the convergence rate of stochastic non-convex optimization under the average smoothness condition. Existing adaptive variance reduction methods are especially designed for average smoothness and do not adapt to standard smoothness. In this paper, we study how one algorithm can adapt to these two settings and the noiseless setting

Natalie Behague, Jack Cromwell

Let $D$ be an oriented graph. For $X \subseteq V(D)$, the inversion of $X$ in $D$ is the oriented graph that is obtained via changing the direction of all edges which have both endpoints in $X$. The inversion number of $D$, $\operatorname{inv}(D)$, is the minimum number of inversions necessary in order to remove all cycles from $D$. A dijoin $D_1 \to D_2$ is

Sangpil Youm, Annabel Stocks Natalias, Hayden Heninger, Leah Jones

Detecting communicative meaning in dialogue requires modeling not only lexical content but also the participant-role structures through which sentiment, emotion, politeness, and formality (SEPF) are expressed. Existing approaches rely on contextual language representations while leaving semantic roles implicit. We introduce a role-aware transformer architect

Matteo Chiatto, Luigi de Luca, Howard A. Stone, Francesco Grasso

The motion of inertial particles suspended in a carrier fluid, together with the dynamics of the underlying vortical structures, is investigated in a square microfluidic T-junction. The coherent structures forming near the branching strongly affect the particle distribution and may promote particle trapping around the T-region. Single-phase numerical simulat

Karsten Walz Jensen, Mie Andersen, Raffaele Cheula

Dopants can tune the activity of zirconia-based CO$_2$ hydrogenation catalysts through changes in adsorbate and transition-state (TS) energetics. Computing these quantities with density functional theory (DFT) becomes expensive when several dopants, reaction intermediates, and TSs must be screened. Here, we further develop the graph-based machine learning me

William Sheffield, Jiahui Chen, Amy Zhang, Alex Warstadt

Discourse particles subtly shape the flow of discourse by reflecting conversational context and interlocutor attitudes, often leaving the truth-conditional meaning untouched. They are also often polyfunctional, with distinct senses which partially overlap with other particles; for example, the exclusive use of English &#34;just&#34; and &#34;only&#34; (&#34;

Nibedita Ghosh

We consider an inexact version of the Schwarz domain decomposition method in $3$D to solve a Laplace problem in a domain that is the union of two overlapping spheres. We solve each subproblem approximately by projecting its boundary data $L^2$-orthogonally onto the space of spherical harmonics of degree at most $N$. The model problem arises from the ddCOSMO

Ben Bouwen, Jorge Castillejos, Jakub Curda

We show that certain classes of unital, simple, separable C*-algebras with comparison are singly generated, including tracially AF algebras and monotracial crossed products of actions on the Cantor space. In particular, we show that simple, separable, tracially AF algebras have generator rank one.

Mingwei Dai, Walter Freeman, Mason Grieb, Jack Laiho

We study the spectrum of two-particle bound states of Kähler-Dirac fermions coupled via gravity in Euclidean Dynamical Triangulations (EDT). We work in the quenched approximation, where the geometry is allowed to fluctuate, but there is no back-reaction from the matter on the geometry. The Kähler-Dirac action respects an exact $U(1)$ symmetry that is related

Jasin Cekinmez, Addison J. Wu, Ryo Mitsuhashi, Linrong Cai

How well can vision-language models (VLMs) reason about what is missing from an image? We study this question with OccludeBench, a manually curated benchmark for recovering fully occluded objects from the visual evidence that remains. OccludeBench distinguishes direct cases, where the hidden object must be recovered from a specific physical cue such as a sha

El Mahdi Chayti, Martin Jaggi

Momentum methods such as STORM and second-order momentum track the gradient by adding a transport correction to momentum, at the cost of an extra oracle call per iteration. We introduce randomized momentum transport, which evaluates the correction with probability $q$ and otherwise uses a cheap prediction, with importance weighting that preserves the conditi

Moumita Mukherjee, Avijit Banerjee, Anton Koval, George Nikolakopoulo

LiDAR-inertial odometry (LIO) is widely used for real-time localization and mapping, but its reliability can degrade substantially in subterranean environments affected by smoke, dust, and other airborne obscurants, whereas radar remains comparatively robust. This article presents a one-class temporal learning framework for LiDAR degradation detection and re

Gon Buzaglo, Guy Kornowski

We present new algorithms for stochastic optimization problems in which the objective is neither smooth nor convex, subject to a convex constraint set. Our algorithms return a $(δ,\varepsilon)$-Goldstein stationary point, as recently defined for constrained problems by Liu et al. (2024), with improved complexities, and are the first to achieve optimal conver

Adarsh Jamadandi, Nicolas Keriven

Discrete graph diffusion models (GDMs) corrupt the graph structure by adding noise independently to nodes and edges under a Markovian noise model. In the unconditional setting without node features, an equivariant denoiser trained on a single graph is able to generate novel graphs and sample-level metrics such as edge overlap and WL kernel similarity find no

Afonso Rodrigues

Aggregate linear demand systems can be micro-founded by a population of heterogeneous consumers making discrete choices. They can also be extended to incorporate income effects. I prove these results and show how to structurally estimate this class of models using only market-level data. The distribution of consumer preferences is recovered by disciplining t

Philipp Westhoff, Sara Murciano, Mattia Moroder

Dissipative state preparation engineers quantum many-body states as steady states of an open-system dynamics. The spatial range of the required dissipation sets both the feasibility of analog implementations and the connectivity and control complexity of digital ones. Yet how this locality quantitatively constrains preparation times remains largely unknown.

Nazar Andrushko, Vera Schmitt, Veronika Solopova

Memes play a central role in modern online communication, especially in contexts of political conflict where visual and textual cues combine to convey persuasive messages. We study persuasive technique detection in Ukrainian memes using vision-language models (VLMs). We introduce UkrMeme, a dataset of 200 organically produced Ukrainian wartime memes annotate

Pramod Padmanabhan, Somnath Maity, Akash Sinha, Vladimir Korepin

We establish that bond Hamiltonian densities, up to local difference terms whose sum vanishes under periodic boundary conditions, lying in the commutant of bond operators, $w_j\pm w_{j+1}$, is a necessary and sufficient condition for a one-dimensional nearest-neighbour Hamiltonian to conserve a uniform or staggered sum of the one-site operators $w_j$. Starti

Changhui Jing, Haochen Tian, Hongchen Li, Tianyu Li

Driving world action models (WAMs) learn from paired trajectories and future observations. Post-training with reinforcement learning allows the policy to explore alternative trajectories and improve planning using driving rewards. However, driving logs contain future videos only for the recorded trajectories, leaving these alternatives without corresponding

Prateek Verma, Benjamin Butter, Jian Zhang, Xintao Wu

Dead cell detection in fluorescence microscopy is essential for phenotypic screening and drug efficacy assessment. However, automated identification remains challenging due to signal variability, optical artifacts, and colocalization ambiguities across multiple channels. Markers that define nuclear boundaries do not neatly coincide spatially or temporally wi

Neha Goswami, John Hover, Renae Galluccio, Yichen Wu

BARseq enables spatially resolved gene-expression profiling across intact tissue, but its sensitivity depends on accurate detection and decoding of dense fluorescent rolonies. Existing spot-calling approaches can be conservative where signals overlap, while supervised deep learning is limited by the lack of reliable manual ground truth in real tissue images.

Ze-Wei Liou, Harris Nover, Amey Naik, Andrii Skliar

State-space models (SSMs) maintain a fixed-size recurrent state, but reading and writing it at every decoding step can bottleneck hybrid models. This eager state management causes large state I/O with low arithmetic intensity, leaves speculative decoding no cheap rollback, and feeds state quantization error into subsequent recurrent updates. We introduce Rep

Michael Hobson, Will Barker, Anthony Lasenby

We investigate the local gravitational source and charges obtained by introducing translations as an internal Stückelberg redundancy in Weyl gauge theory. The internal-$T(4)$-invariant dressed coframe identifies the source of the translational potential with the existing coframe variational derivative, without introducing new dynamics. We derive an excitatio

Magdalena Trędowicz, Piotr Kubaty, Arkadiusz Lewicki, Jacek Tabor

Selective state-space models are an efficient approach to long-sequence video modeling. However, their recurrent computation makes it difficult to determine which video tokens drive a prediction. Existing attribution methods often require additional backward passes or repeated model evaluations, making them substantially more expensive than the forward infer

Shenyu Qin, Chihao Zhang, Yuhao Zhang, Zixuan Zhu

Pandora&#39;s problem is a fundamental model of sequential search in which opening each box incurs a cost. We study the variant with deadlines, where each box has a publicly known deadline for both opening and selection. We prove that a simple threshold strategy achieves a $1/2$-approximation to the optimal expected utility, improving the previous guarantee

S. P. van den Heever, D. J. van der Walt

The brightest 6.7 GHz methanol maser in the Galaxy is associated with the high-mass star-forming region (HMSFR) G009.62+0.20E. Additionally, this methanol maser shows periodic flaring observed at 6.7 and 12.2 GHz. Several models have been hypothesized to explain the periodicity, including periodic accretion, spiral shocks in the gaps of circumbinary disks, p

El Mahdi Chayti, Alireza Mirrokni, Martin Jaggi

Multiple-gradient descent is typically formulated in Euclidean geometry, whereas matrix-aware optimizers use directions induced by other norms. We analyze a single-loop multi-objective method combining a norm-constrained linear minimization oracle with entropic updates of task weights. For any finite-dimensional norm pair, we establish an $O(T^{-1/2})$ ergod

Tom Philipp Backert, Mohammad D. Soorati, Javad Ghofrani

Multi-agent reinforcement learning (MARL) has become a prominent research direction, particularly within collective robotics, where the ARGoS simulator is widely used to study emergent swarm behavior. Yet despite this growth, systematic support for reuse and reproducibility in MARL-based swarm robotics remains limited and insufficiently standardized: a singl

R. Alfaro, C. Alvarez, E. Anita-Rangel, M. Araya

Previous observations of the DA 495 region, which contains the pulsar wind nebula (PWN) DA 495 and other very-high-energy (VHE; $>100~\textrm{GeV}$) gamma-ray emitters, showed inconsistent results across different VHE experiments, including differences in the measured flux of DA 495 and in the number of reported sources. Our new dedicated analysis using 3071

Carlos Cordoba-Caycedo, Dario Garcia

This paper studies pseudofinite abelian groups and counting properties of definable sets in finite abelian groups and their ultraproducts. First, we characterize pseudofinite abelian groups in terms of Szmielew invariants and, building on results of Macintyre, extend this analysis to obtain a characterization of pseudofinite $ω$-stable abelian groups. We the

G. Kazakevich, R. P. Johnson, I. Gonin, V. Yakovlev

Powerful CW magnetrons were suggested for feeding RF cavities of superconducting accelerators because they provide higher efficiency and lower RF power cost than the traditionally used klystrons, IOTs, or solid-state amplifiers. RF amplifiers driven by a master oscillator serve as coherent RF sources. CW magnetrons are regenerative RF self-oscillators with a

Henry Bourne, Robert F. Allison

What can an adversary recover from a model trained with DP-SGD? Its $(\varepsilon,δ)$ guarantee does not answer this question directly. We derive a distortion bound that can be computed before training and applies even to an adversary who observes the parameter trajectory during training, knows every other training record and the population distribution from

Mahsa Salmani, Sayeh Sharify, Hesham Mostafa

Low-bit inference on accelerators with native 4-bit support requires quantizing both weights and activations to realize the full throughput gains these formats provide. While post-training weight quantization has been extensively studied, activation quantization remains considerably more challenging and typically requires calibration data. In this work, we i

Ali Shibli, Andrea Nascetti, Yifang Ban

Geospatial foundation models can support disaster damage assessment, but high accuracy on familiar disasters may hide shortcut learning: prediction from event or scene characteristics rather than evidence that transfers to a new disaster. We study embeddings from TerraMind, Prithvi-EO-2.0, DOFA, and DINOv3 on the multi-disaster datasets xView2 and BRIGHT. Ho

Adarsh Jamadandi, Nicolas Keriven, Aline Roumy

Discrete graph diffusion models corrupt graph structure through independent edge updates with a Markovian noise model. Combined with the exchangeability requirement on the output distribution, the terminal prior is necessarily an Erdős--Rényi (E-R) random graph. This has a fundamental consequence for unconditional graph generation without node features: in t

Manuel Hülskamp, Julius Kobialka, Emanuel Sommer, Thomas Nagler

Prior-data fitted networks (PFNs) are trained to approximate the Bayesian posterior predictive distribution (PPD) of a synthetic prior, yet results about the approximation quality are surprisingly thin. Taking an empirical risk minimization perspective, we decompose the deployment error of PFNs into prior, context, and amortization error, and measure the lat

Haonan Wang, Yuchen Wang, Jingyu Lu, Qixiang Zhang

Decoding visual experience from fMRI is important for understanding human perception and developing brain-computer interfaces. Recent generative decoding methods can reconstruct plausible images and videos, but visual plausibility alone does not ensure that the output is faithfully grounded in brain activity. In this work, we propose Neurons++, a visual-stre

Bertram Bitsch

Planet interior models have revealed that warm Jupiters should harbor large heavy element masses (up to 30-40% of the planetary mass). Within the pebble drift and evaporation scenario, inward drifting pebbles can evaporate and enrich the inner disc with vapor to super-stellar values, accreteable by giant planets. As H$_2$O ice evaporates in the inner disc, w

Hamid Hassani, Sonia M. Heemstra de Groot, Ignas G. Niemegeers, Kishor C. Joshi

Achieving low delay and high data rate communication in 5G requires preventing persistent queue backlogs at path bottlenecks. A key indicator of queue buildup is an increase in one-way delay (OWD) of packets. However, accurately measuring OWD is challenging due to synchronization issues between the sender and receiver. Moreover, using OWD to infer the onset

Qituan Shangguan, Bosen Xu, Shuai Wang

Speaker verification (SV) systems now achieve very low error rates on standard public protocols, but those results do not fully describe deployment generalization. VoxCeleb-style evaluations remain useful anchors for English in-the-wild media speech. Scenario-specific evaluations and challenges are also valuable. However, neither setting directly shows wheth

Kristijan Atanasov, Frederik Mallmann-Trenn, Carmine Ventre

We study a dynamic lending game with limited but reusable supply. Borrower arrivals and loan durations are stochastic. Demand is allocated cheapest-first, subject to available supply, so a lender&#39;s quote affects both its current payoff and its ability to serve future borrowers. Motivated by industry practice, we study this problem through the lens of inf

Daming Yang, Joseph F. Hennawi, Rychard Bouwens, Timo Kist

Most known quasars at $z>6$ were selected through their rest-frame UV emission. Only a small number of reddened quasars have been identified at these redshifts, leaving their nuclear and host-galaxy properties poorly constrained. We present JWST/NIRSpec spectroscopy and ALMA band 6 observations of the quasar J1440+0019 at $z=6.5488$, revealing its dust-redde

Yiming Liang, Tianyu Xiao, Hiroshi Ishikawa

While novel view synthesis methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have shown impressive performance on common scenes, they often struggle with capturing view-dependent specular effects, particularly near-field reflections. Existing methods utilize environment maps, which only represent lights from infinity, or environment

Nkechi J. Akinwale, Seokki Lee

Expressing ML algorithms as recursive aggregate SQL queries over distributed systems enables large-scale declarative data analytics, yet no existing system supports scalable provenance capture, necessary for ML explainability. We present ProvQML, a provenance-aware recursive aggregate SQL engine that efficiently captures fine-grained provenance while evaluat

Chenxu Zhang, Yaorui Shi, Wenlong Zhang, Jianchao Tan

Training long-horizon language model agents requires assigning credit to intermediate decisions from sparse terminal feedback. Within a finite group of sibling rollouts, useful action comparisons depend on their context, evidence for both alternatives, and how that evidence enters learning. We propose Matched-Context Group Policy Optimization (MCGPO), a proc

J. Gurzó, G. Fejős

A novel physics-informed neural networks (PINNs) based approach for solving functional renormalization group flow equations is presented. We investigate the finite temperature phase transition in $O(N)$ models without relying on the large-$N$ approximation, which was used in earlier attempts with similar methods. We argue that these previous solutions cannot

Xi Chen

We prove scattering in $L^2(\mathbb{R}^2)$ for real-valued solutions of the KP-II equation with localized initial data. The hypotheses cover all Schwartz functions, data with a half spatial moment in $B^2_{1,1}$, and a class of weighted tensor Besov spaces allowing low regularity in either coordinate. The proof uses the scalar $\bar\partial$ equation associa

Mattia Buratti, François Genoud, Vladimir Georgiev

This work is concerned with the focusing quintic nonlinear Schrödinger equation on a star graph with two edges, with a delta interaction of arbitrary strength at the vertex. We construct a non-radial, minimal mass, finite time blow-up solution, whose singularity forms at an arbitrarily prescribed interior point of one edge. Our approach is based on a suitabl

Hao Cao, Jian-Qiang Hu, Jiaqiao Hu

We present a simulation-based method for the estimation, sensitivity analysis, and optimization of distortion risk measures (DRMs), a class of performance measures that capture a wide range of risk attitudes through an underlying distortion function. Existing simulation approaches often approximate DRMs using numerical partitions or empirical distributions.

Emily Xia Meng, Xuanyu Yang, Junaid Hasan, Michael Ruofan Zeng

Basic mathematical tasks like modular addition have been a valuable tool for illuminating aspects of neural network behavior that are challenging to see in noisy, unverifiable, large-scale, real-world settings. These studies have typically focused on a single task. However, modern language models are exposed to a vast number of tasks despite being trained wi

Hamid Hassani, Sonia M. Heemstra de Groot, Ignas G. Niemegeers, Kishor C. Joshi

Many 6G use cases such as tactile Internet and eXtended reality applications would simultaneously require a high data rate and low latency wireless connectivity.It is increasingly being realized that the current transport protocols relying on packet loss and delay are insufficient to satisfy the data rate and latency requirements of such applications. To add

Logan H. Grant, Ana Bensabat, Eva Pospivsilova, Joost de Graaf

Frustration generated by competing interactions can stabilise patterns with tunable lengthscales which evade indefinite coarsening and macroscopic phase separation. Here we investigate a charged binary fluid in which the tendency toward phase separation is opposed by screened electrostatic repulsion. Combining analytics and numerical simulations, we identify