Tootfinder

Pair state transfer in tensor product and double cover
Ming Jiang, Xiaogang Liu, Jing Wang
https://arxiv.org/abs/2509.18858 https://arxiv.org/pdf/2509.1885…

BatchTNMC: Efficient sampling of two-dimensional spin glasses using tensor network Monte Carlo
Tao Chen, Jingtong Zhang, Jing Liu, Youjin Deng, Pan Zhang
https://arxiv.org/abs/2509.19006

BatchTNMC: Efficient sampling of two-dimensional spin glasses using tensor network Monte Carlo
Efficient sampling of two-dimensional statistical physics systems remains a central challenge in computational statistical physics. Traditional Markov chain Monte Carlo (MCMC) methods, including cluster algorithms, provide only partial solutions, as their efficiency collapses for large systems in the presence of frustration and quenched disorder. The recently proposed Tensor Network Monte Carlo (TNMC) method offers a promising alternative, yet its original implementation suffers from inefficien…

haha "Thanks very much for you time"
What's a Tensor?
https://www.youtube.com/watch?v=f5liqUk0ZTw

Modern search demands scalable personalisation. At this year's Berlin Buzzwords, Piotr Kobziakowski discussed how Vespa's multi-stage ranking and tensor framework can be used for hybrid queries, multimodal retrieval, and real-time machine learning. Learn how to deploy low-latency, high-relevance search systems at petabyte scale.
Watch the full session: https://youtu.be/362U7alsfuI?si=W5SGEO8MIa8vywGO
Berlin Buzzwords returns on 7-9 June 2026! Get 36% off with our Trust Us Ticket: https://tickets.plainschwarz.com/bbuzz26/c/8Hvk0ZvJA/

Tensor Reduction of Sunset by Generating Function
Xinhe Chen, Bo Feng, Liang Zhang
https://arxiv.org/abs/2509.18730 https://arxiv.org/pdf/2509.18730…

Tensor Train Completion from Fiberwise Observations Along a Single Mode
Shakir Showkat Sofi, Lieven De Lathauwer
https://arxiv.org/abs/2509.18149 https://a…

Crosslisted article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Tensor Train Completion from Fiberwise Observations Along a Single Mode
Shakir Showkat Sofi, Lieven De Lathauwer

Had a great time presenting our paper “Domain-specific tensor languages” today at #ICFP2025 in Singapore 🇸🇬
The images attached are screenshots from the paper as teasers — diagrams for derivative rules, the Riemann curvature 4-tensor in Einstein’s index notation, and the same in diagram form.
📄 Slides:

On the Viability of Weak Energy Condition Violation in Parity-Violating Electrodynamics
Tao Huang
https://arxiv.org/abs/2509.18188 https://arxiv.org/pdf/25…

Quantum Howe duality and Schur duality of type AIII
Weinan Zhang
https://arxiv.org/abs/2509.18982 https://arxiv.org/pdf/2509.18982

Impact of Heterogeneity on Scalar Flux Variance Relations Across Diverse Ecosystems
Tyler Waterman, Ivana Stiperski, Laura Torres-Rojas, Marc Calaf
https://arxiv.org/abs/2509.18385

Quantum-electrodynamical time-dependent density functional theory description of molecules in optical cavities
Yetmgeta Aklilu, Matthew Shepherd, Cody L. Covington, Kalman Varga
https://arxiv.org/abs/2509.19036

A continued fraction approximation for the effective elasticity tensor of two-dimensional polycrystals as a function of the crystal elasticity tensor
Graeme W. Milton
https://arxiv.org/abs/2510.10297

A continued fraction approximation for the effective elasticity tensor of two-dimensional polycrystals as a function of the crystal elasticity tensor
For two-dimensional polycrystals the effective elasticity tensor $C_*$ as a function $C_*(C_0)$ of the elasticity tensor $C_0$ of the constituent crystal is considered. It is shown that this function can be approximated by one with a continued fraction expansion resembling that associated with a class of microstructure known as sequential laminates. These are hierarchical microstructures defined inductively. Rank 0 sequential laminates are simply rotations of the pure crystal. Rank $j$ sequenti…

Magnetization by Rotation: Spin and Chiral Condensates in the NJL Model
Lutz Kiefer, Ashutosh Dash, Dirk H. Rischke
https://arxiv.org/abs/2509.18881 https://

Magnetization by Rotation: Spin and Chiral Condensates in the NJL Model
The role of spin degrees of freedom in the quark-gluon plasma (QGP) has attracted significant interest in recent years. Spin hydrodynamics extends conventional hydrodynamics by incorporating spin via the spin tensor. In the mean-field limit of the Nambu-Jona-Lasinio (NJL) model under rigid rotation, spin degrees of freedom manifest naturally as axial-vector, or spin, condensate. We investigate the interplay between chiral and spin condensates in this framework. While rotation typically suppress…

ACT: Automatically Generating Compiler Backends from Tensor Accelerator ISA Descriptions
Devansh Jain, Akash Pardeshi, Marco Frigo, Krut Patel, Kaustubh Khulbe, Jai Arora, Charith Mendis
https://arxiv.org/abs/2510.09932

ACT: Automatically Generating Compiler Backends from Tensor Accelerator ISA Descriptions
Tensor compilers play a key role in enabling high-performance implementations of deep learning workloads. These compilers rely on existing CPU and GPU code generation backends to generate device-specific code. Recently, many tensor accelerators (neural processing units) have been proposed to further accelerate these workloads. Compared to commodity hardware, however, most of the proposed tensor accelerators do not have compiler backends with code generation support. Moreover, the accelerator de…

The tensor product of p-adic Hilbert spaces
Paolo Aniello, Lorenzo Guglielmi, Stefano Mancini, Vincenzo Parisi
https://arxiv.org/abs/2510.07504 https://arx…

The tensor product of p-adic Hilbert spaces
In the framework of quantum mechanics over a quadratic extension of the ultrametric field of p-adic numbers, we introduce a notion of tensor product of p-adic Hilbert spaces. To this end, following a standard approach, we first consider the algebraic tensor product of p-adic Hilbert spaces. We next define a suitable norm on this linear space. It turns out that, in the p-adic framework, this norm is the analogue of the projective norm associated with the tensor product of real or complex normed …

Tensor Logic: The Language of AI
Pedro Domingos
https://arxiv.org/abs/2510.12269 https://arxiv.org/pdf/2510.12269…

Tensor Logic: The Language of AI
Progress in AI is hindered by the lack of a programming language with all the requisite features. Libraries like PyTorch and TensorFlow provide automatic differentiation and efficient GPU implementation, but are additions to Python, which was never intended for AI. Their lack of support for automated reasoning and knowledge acquisition has led to a long and costly series of hacky attempts to tack them on. On the other hand, AI languages like LISP an Prolog lack scalability and support for learn…

Efficient tensor-network simulations of weakly-measured quantum circuits
Darren Pereira, Leonardo Banchi
https://arxiv.org/abs/2510.07211 https://arxiv.org…

Efficient tensor-network simulations of weakly-measured quantum circuits
We present a tensor-network-based method for simulating a weakly-measured quantum circuit. In particular, we use a Markov chain to efficiently sample measurements and contract the tensor network, propagating their effect forward along the spatial direction. Applications of our algorithm include validating quantum computers (capable of mid-circuit measurements) in regimes of easy classical simulability, and studying generative-machine-learning applications, where sampling from complex stochastic…

The border rank of the $4 \times 4$ determinant tensor is twelve
Jong In Han, Jeong-Hoon Ju, Yeongrak Kim
https://arxiv.org/abs/2510.11051 https://arxiv.or…

The border rank of the $4 \times 4$ determinant tensor is twelve
We show that the border rank of the $4 \times 4$ determinant tensor is at least $12$ over $\mathbb{C}$, using the fixed ideal theorem introduced by Buczyńska-Buczyński and the method by Conner-Harper-Landsberg. Together with the known upper bound, this implies that the border rank is exactly $12$.

Smooth Trade-off for Tensor PCA via Sharp Bounds for Kikuchi Matrices
Pravesh K. Kothari, Jeff Xu
https://arxiv.org/abs/2510.03061 https://arxiv.org/pdf/25…

Smooth Trade-off for Tensor PCA via Sharp Bounds for Kikuchi Matrices
In this work, we revisit algorithms for Tensor PCA: given an order-$r$ tensor of the form $T = G+λ\cdot v^{\otimes r}$ where $G$ is a random symmetric Gaussian tensor with unit variance entries and $v$ is an unknown boolean vector in $\{\pm 1\}^n$, what's the minimum $λ$ at which one can distinguish $T$ from a random Gaussian tensor and more generally, recover $v$? As a result of a long line of work, we know that for any $\ell \in \N$, there is a $n^{O(\ell)}$ time algorithm that succeeds whe…

On decomposability and subdifferential of the tensor nuclear norm
Jiewen Guan, Bo Jiang, Zhening Li
https://arxiv.org/abs/2510.04647 https://arxiv.org/pdf/…

On decomposability and subdifferential of the tensor nuclear norm
We study the decomposability and the subdifferential of the tensor nuclear norm. Both concepts are well understood and widely applied in matrices but remain unclear for higher-order tensors. We show that the tensor nuclear norm admits a full decomposability over specific subspaces and determine the largest possible subspaces that allow the full decomposability. We derive novel inclusions of the subdifferential of the tensor nuclear norm and study its subgradients in a variety of subspaces of in…

Guaranteed Noisy CP Tensor Recovery via Riemannian Optimization on the Segre Manifold
Ke Xu, Yuefeng Han
https://arxiv.org/abs/2510.00569 https://arxiv.org…

Guaranteed Noisy CP Tensor Recovery via Riemannian Optimization on the Segre Manifold
Recovering a low-CP-rank tensor from noisy linear measurements is a central challenge in high-dimensional data analysis, with applications spanning tensor PCA, tensor regression, and beyond. We exploit the intrinsic geometry of rank-one tensors by casting the recovery task as an optimization problem over the Segre manifold, the smooth Riemannian manifold of rank-one tensors. This geometric viewpoint yields two powerful algorithms: Riemannian Gradient Descent (RGD) and Riemannian Gauss-Newton (R…

Compressed Bayesian Tensor Regression
Roberto Casarin, Radu Craiu, Qing Wang
https://arxiv.org/abs/2510.01861 https://arxiv.org/pdf/2510.01861

Compressed Bayesian Tensor Regression
To address the common problem of high dimensionality in tensor regressions, we introduce a generalized tensor random projection method that embeds high-dimensional tensor-valued covariates into low-dimensional subspaces with minimal loss of information about the responses. The method is flexible, allowing for tensor-wise, mode-wise, or combined random projections as special cases. A Bayesian inference framework is provided featuring the use of a hierarchical prior distribution and a low-rank re…

Completely Positive Biquadratic Tensors
Liqun Qi, Chunfeng Cui, Haibin Chen, Yi Xu
https://arxiv.org/abs/2510.10972 https://arxiv.org/pdf/2510.10972…

Completely Positive Biquadratic Tensors
In this paper, we systemically introduce completely positive biquadratic (CPB) tensors and copositive biquadratic tensors. We show that all weakly CPB tensors are sum of squares tensors, the CPB tensor cone and the copositive biquadratic tensor cone are dual cone to each other. We also show that the outer product of two completely positive matrices is a CPB tensor, and the outer product of two copositive matrices is a copositive biquadratic tensor. We then study two easily checkable subclasses …

Way-below Relation and Tensor Products
Cristian Ivanescu, Hunter Labrecque
https://arxiv.org/abs/2510.11947 https://arxiv.org/pdf/2510.11947

Way-below Relation and Tensor Products
We establish the preservation of the way-below relation with respect to the tensor product.

Learning Mixtures of Linear Dynamical Systems (MoLDS) via Hybrid Tensor-EM Method
Lulu Gong, Shreya Saxena
https://arxiv.org/abs/2510.06091 https://arxiv.o…

Learning Mixtures of Linear Dynamical Systems (MoLDS) via Hybrid Tensor-EM Method
Mixtures of linear dynamical systems (MoLDS) provide a path to model time-series data that exhibit diverse temporal dynamics across trajectories. However, its application remains challenging in complex and noisy settings, limiting its effectiveness for neural data analysis. Tensor-based moment methods can provide global identifiability guarantees for MoLDS, but their performance degrades under noise and complexity. Commonly used expectation-maximization (EM) methods offer flexibility in fitting…

Crosslisted article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
[1/1]:
- Tensor Train Completion from Fiberwise Observations Along a Single Mode
Shakir Showkat Sofi, Lieven De Lathauwer

Universal properties of Delannoy categories
Kevin Coulembier, Nate Harman, Andrew Snowden
https://arxiv.org/abs/2510.10317 https://arxiv.org/pdf/2510.10317…

Universal properties of Delannoy categories
Recently, the second and third authors introduced a new symmetric tensor category $\underline{\mathrm{Perm}}(G, μ)$ associated to an oligomorphic group $G$ with a measure $μ$. When $G$ is the group of order preserving self-bijections of the real line there are four such measures, and the resulting tensor categories are called the Delannoy categories. The first Delannoy category is semi-simple, and was studied in detail by Harman, Snowden, and Snyder. We give universal properties for all four …

Tensor-current contributions to B Anomalies
Qiaoyi Wen, Fanrong Xu
https://arxiv.org/abs/2510.05541 https://arxiv.org/pdf/2510.05541

Tensor-current contributions to B Anomalies
Tensor-current operators, potentially generated by scalar leptoquarks in grand unified theories (GUTs), are among the plausible new physics (NP) candidates suggested by the anomalies observed in $B$-meson decays. As experimental data continue to accumulate, exploring this possibility remains timely and well motivated. In this work, we present a systematic analysis of representative tensor-current Wilson coefficients ($C_T, C_{T5}$) in $b \to s \ell^+ \ell^-$ transitions. By incorporating contri…

Tensor tomography on asymptotically hyperbolic surfaces
Nikolas Eptaminitakis, Fran\c{c}ois Monard, Yuzhou Joey Zou
https://arxiv.org/abs/2510.04144 https://

Tensor tomography on asymptotically hyperbolic surfaces
We initiate a study of the inversion of the geodesic X-ray transform $I_m$ over symmetric $m$-tensor fields on asymptotically hyperbolic surfaces. This operator has a non-trivial kernel whenever $m\ge 1$. To propose a gauge representative to be reconstructed from X-ray data, we first prove a "tt-potential-conformal" decomposition theorem for $m$-tensor fields (where "tt" stands for transverse traceless), previously used in integral geometry on compact Riemannian manifolds with boundary in Shara…

Improving Runtime Performance of Tensor Computations using Rust From Python
Kimmie Harding, Daniel M. Dunlavy
https://arxiv.org/abs/2510.01495 https://arxi…

Improving Runtime Performance of Tensor Computations using Rust From Python
In this work, we investigate improving the runtime performance of key computational kernels in the Python Tensor Toolbox (pyttb), a package for analyzing tensor data across a wide variety of applications. Recent runtime performance improvements have been demonstrated using Rust, a compiled language, from Python via extension modules leveraging the Python C API -- e.g., web applications, data parsing, data validation, etc. Using this same approach, we study the runtime performance of key tensor …

Tensor-based compression of the sea temperature data
Ilya Kosolapov, Tatiana Sheloput, Sergey Matveev
https://arxiv.org/abs/2510.09778 https://arxiv.org/pd…

Tensor-based compression of the sea temperature data
In this work we investigate efficient data compression for spatiotemporal Black, Azov and Marmara Seas temperature tensors that contain significant number of missing values. These tensors have a complex structure influenced by the coastlines and bathymetry, as well as temporal temperature changes. While such missing data typically provokes utilization of tensor completion algorithms, we demonstrate that standard SVD-based compression approaches (including the Tucker, Tensor-Train (TT) and Quant…

A rigorous coupled-wave analysis of birefringent holographic gratings with periodically-modulated dielectric tensor along an in-plane direction and tensor variations in the thickness direction
Masud Mansuripur, Per Kristen Jakobsen
https://arxiv.org/abs/2510.01214

A rigorous coupled-wave analysis of birefringent holographic gratings with periodically-modulated dielectric tensor along an in-plane direction and tensor variations in the thickness direction
Diffraction of light upon interaction with thick slabs of a dielectric material having a periodic modulation of its refractive index (or dielectric tensor) is typically studied with the aid of the method known as the rigorous coupled-wave analysis (RCWA). The method involves solving Maxwell's equations for a large number of coupled electromagnetic plane-waves inside the dielectric slab, then matching the boundary conditions at the interface between the incidence medium and the slab, as well as …

Reconstruction of tensor categories of type $G_2$
Lilit Martirosyan, Hans Wenzl
https://arxiv.org/abs/2510.09922 https://arxiv.org/pdf/2510.09922

Reconstruction of tensor categories of type $G_2$
We prove that any non-symmetric ribbon tensor category $\mathcal{C}$ with the fusion rules of the compact group of type $G_2$ needs to be equivalent to the representation category of the corresponding Drinfeld-Jimbo quantum group for $q$ not a root of unity. We also prove an analogous result for the corresponding finite fusion tensor categories.

Towards fairer public transit: Real-time tensor-based multimodal fare evasion and fraud detection
Peter Wauyo, Dalia Bwiza, Alain Murara, Edwin Mugume, Eric Umuhoza
https://arxiv.org/abs/2510.02165

Towards fairer public transit: Real-time tensor-based multimodal fare evasion and fraud detection
This research introduces a multimodal system designed to detect fraud and fare evasion in public transportation by analyzing closed circuit television (CCTV) and audio data. The proposed solution uses the Vision Transformer for Video (ViViT) model for video feature extraction and the Audio Spectrogram Transformer (AST) for audio analysis. The system implements a Tensor Fusion Network (TFN) architecture that explicitly models unimodal and bimodal interactions through a 2-fold Cartesian product. …

End-to-end quantum algorithms for tensor problems
Enrico Fontana, Sivaprasad Omanakuttan, Junhyung Lyle Kim, Joseph Sullivan, Michael Perlin, Ruslan Shaydulin, Shouvanik Chakrabarti
https://arxiv.org/abs/2510.07273

End-to-end quantum algorithms for tensor problems
We present a comprehensive end-to-end quantum algorithm for tensor problems, including tensor PCA and planted kXOR, that achieves potential superquadratic quantum speedups over classical methods. We build upon prior works by Hastings~(\textit{Quantum}, 2020) and Schmidhuber~\textit{et al.}~(\textit{Phys.~Rev.~X.}, 2025), we address key limitations by introducing a native qubit-based encoding for the Kikuchi method, enabling explicit quantum circuit constructions and non-asymptotic resource esti…

Google Pixel 10 Pro Fold review: nice display and most AI features run on device, but cameras are a step down from other Pro Pixels, Tensor G5 shows small gains (Ryan Whitwam/Ars Technica)
https://arstechnica.com/gadgets/2025/10/google-pixel…

Google Pixel 10 Pro Fold review: The ultimate Google phone
Google delivers another phone that is slightly better than its predecessor—is that enough?

Structured covariance estimation via tensor-train decomposition
Artsiom Patarusau, Nikita Puchkin, Maxim Rakhuba, Fedor Noskov
https://arxiv.org/abs/2510.08174 https://

Structured covariance estimation via tensor-train decomposition
We consider a problem of covariance estimation from a sample of i.i.d. high-dimensional random vectors. To avoid the curse of dimensionality we impose an additional assumption on the structure of the covariance matrix $Σ$. To be more precise we study the case when $Σ$ can be approximated by a sum of double Kronecker products of smaller matrices in a tensor train (TT) format. Our setup naturally extends widely known Kronecker sum and CANDECOMP/PARAFAC models but admits richer interaction acros…

Efficient and accurate tensor network algorithm for Anderson impurity problems
Zhijie Sun, Zhenyu Li, Chu Guo
https://arxiv.org/abs/2510.11459 https://arxi…

Efficient and accurate tensor network algorithm for Anderson impurity problems
The Anderson impurity model (AIM) is of fundamental importance in condensed matter physics to study strongly correlated effects. However, accurately solving its long-time dynamics still remains a great numerical challenge. An emergent and rapidly developing numerical strategy to solve the AIM is to represent the Feynman-Vernon influence functional (IF), which encodes all the bath effects on the impurity dynamics, as a matrix product state (MPS) in the temporal domain. The computational cost of …

Tensor Product Decompositions, Limits in Excellent Filtrations, Affine Weyl Group Orbits, and Tableaux Counting
Laura Estivalez, Adriano Moura
https://arxiv.org/abs/2510.04697 h…

Tensor Product Decompositions, Limits in Excellent Filtrations, Affine Weyl Group Orbits, and Tableaux Counting
We express the outer multiplicities in the tensor products of two fundamental simple modules for an affine Kac-Moody algebra of type $A$ in terms of counting certain sets of multipartitions by exploring the stabilizing limits of certain excellent filtrations. This extends for all ranks a previously obtained result by Jakelić and the second author for rank $1$. The same outer multiplicities were previously computed by Misra and Wilson in terms of counting certain sets of tableaux. By comparing …

DisCoCLIP: A Distributional Compositional Tensor Network Encoder for Vision-Language Understanding
Kin Ian Lo, Hala Hawashin, Mina Abbaszadeh, Tilen Limback-Stokin, Hadi Wazni, Mehrnoosh Sadrzadeh
https://arxiv.org/abs/2509.21287

DisCoCLIP: A Distributional Compositional Tensor Network Encoder for Vision-Language Understanding
Recent vision-language models excel at large-scale image-text alignment but often neglect the compositional structure of language, leading to failures on tasks that hinge on word order and predicate-argument structure. We introduce DisCoCLIP, a multimodal encoder that combines a frozen CLIP vision transformer with a novel tensor network text encoder that explicitly encodes syntactic structure. Sentences are parsed with a Combinatory Categorial Grammar parser to yield distributional word tensors…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[8/8]:
- TC-GS: A Faster Gaussian Splatting Module Utilizing Tensor Cores
Liao, Ding, Cui, Gong, Hu, Wang, Li, Zhang, Wang, Fu

Scalar-tensor theories in the Lyra geometry: Invariance under local transformations of length units and the Jordan-Einstein frame conundrum
E. C. Valad\~ao, Felipe Sobrero, Santiago Esteban Perez Bergliaffa
https://arxiv.org/abs/2510.08433

Scalar-tensor theories in the Lyra geometry: Invariance under local transformations of length units and the Jordan-Einstein frame conundrum
The Lyra geometry provides an interesting approach to develop purely geometrical scalar-tensor theories due to the natural presence of the Lyra scale function. This paper explores further the scale function source term to construct a theory on Lyra manifolds which contains proper generalizations of both Brans-Dicke gravity and the Einstein-Gauss-Bonnet scalar-tensor theory. It is shown that the symmetry group of gravitational theories on the Lyra geometry comprises not only coordinate transform…

Tensor Completion via Monotone Inclusion: Generalized Low-Rank Priors Meet Deep Denoisers
Peng Chen, Deliang Wei, Jiale Yao, Fang Li
https://arxiv.org/abs/2510.12425 https://

Tensor Completion via Monotone Inclusion: Generalized Low-Rank Priors Meet Deep Denoisers
Missing entries in multi dimensional data pose significant challenges for downstream analysis across diverse real world applications. These data are naturally modeled as tensors, and recent completion methods integrating global low rank priors with plug and play denoisers have demonstrated strong empirical performance. However, these approaches often rely on empirical convergence alone or unrealistic assumptions, such as deep denoisers acting as proximal operators of implicit regularizers, whic…

Diffusion-Guided Renormalization of Neural Systems via Tensor Networks
Nathan X. Kodama
https://arxiv.org/abs/2510.06361 https://arxiv.org/pdf/2510.06361…

Diffusion-Guided Renormalization of Neural Systems via Tensor Networks
Far from equilibrium, neural systems self-organize across multiple scales. Exploiting multiscale self-organization in neuroscience and artificial intelligence requires a computational framework for modeling the effective non-equilibrium dynamics of stochastic neural trajectories. Non-equilibrium thermodynamics and representational geometry offer theoretical foundations, but we need scalable data-driven techniques for modeling collective properties of high-dimensional neural networks from partia…

Phase structure analysis of 2d lattice CP(1) model with $\theta$ term using tensor renormalization group method
Hayato Aizawa, Shinji Takeda, Yusuke Yoshimura
https://arxiv.org/abs/2510.06624

Phase structure analysis of 2d lattice CP(1) model with $θ$ term using tensor renormalization group method
We investigate the phase structure of a two-dimensional lattice CP(1) model with a $θ$ term. In particular, we aim to identify a critical region expected to exist along a $θ=π$ line. To explore the phase structure non-perturbatively and avoid the sign problem, we employ the tensor renormalization group method. We make two improvements compared to previous tensor network studies. The first improvement involves refining the initial tensor. Specifically, we construct it using a quadrature metho…

Spatial uniformity of g-tensor and spin-orbit interaction in germanium hole spin qubits
Inga Seidler, Bence Het\'enyi, Lisa Sommer, Leonardo Massai, Konstantinos Tsoukalas, Eoin G. Kelly, Alexei Orekhov, Michele Aldeghi, Stephen W. Bedell, Stephan Paredes, Felix J. Schupp, Matthias Mergenthaler, Gian Salis, Andreas Fuhrer, Patrick Harvey-Collard
https://

Spatial uniformity of g-tensor and spin-orbit interaction in germanium hole spin qubits
Holes in Ge/SiGe heterostructures are now a leading platform for semiconductor spin qubits, thanks to the high confinement quality, two-dimensional arrays, high tunability, and larger gate structure dimensions. One limiting factor for the operation of large arrays of qubits is the considerable variation in qubit frequencies or properties resulting from the strongly anisotropic $g$-tensor. We study the $g$-tensors of six and seven qubits in an array with a Y geometry across two devices. We repor…

Scalarized Hot Neutron Stars Containing Hyperons and $\Delta$-Resonances in Different Evolution Regimes
Fahimeh Rahimi, Zeinab Rezaei, Adamu Issifu
https://arxiv.org/abs/2510.05302

Scalarized Hot Neutron Stars Containing Hyperons and $Δ$-Resonances in Different Evolution Regimes
Scalar-tensor gravity models are among the prime candidates to explain cosmic acceleration, and compact stars provide unique laboratories for testing such theories. Predictions of scalar-tensor gravity in compact stars can be examined during the evolution of neutron stars. Spontaneous scalarization in relativistic stars is influenced by different properties of stellar matter in various evolution regimes. In the present study, we investigate the scalarization of neutron stars in different stages…

Tensor Network Loop Cluster Expansions for Quantum Many-Body Problems
Johnnie Gray, Gunhee Park, Glen Evenbly, Nicola Pancotti, Garnet Kin-Lic Chan
https://arxiv.org/abs/2510.05647

Tensor Network Loop Cluster Expansions for Quantum Many-Body Problems
We analyze the tensor network loop cluster expansion, introduced in arXiv:2504.07344 as a systematic correction to belief propagation, in the context of general quantum many-body problems. We provide numerical examples of the accuracy and practical applicability of the approach for the computation of ground-state observables for high bond dimension tensor networks, in two- and three-dimensions, with open and periodic boundary conditions, and for spin and fermion problems.

Geometric Construction of Quiver Tensor Products
Daigo Ito, John S. Nolan
https://arxiv.org/abs/2510.05277 https://arxiv.org/pdf/2510.05277

Geometric Construction of Quiver Tensor Products
By a classic theorem of Beilinson, the perfect derived category $\operatorname{Perf}(\mathbb{P}^n)$ of projective space is equivalent to the category of derived representations of a certain quiver with relations. The vertex-wise tensor product of quiver representations corresponds to a symmetric monoidal structure $\otimes_{\mathsf{Q}}$ on $\operatorname{Perf}(\mathbb{P}^n)$. We prove that, for a certain choice of equivalence, the symmetric monoidal structure $\otimes_{\mathsf{Q}}$ may be descr…

Oriented Category Theory
David Gepner, Hadrian Heine
https://arxiv.org/abs/2510.10504 https://arxiv.org/pdf/2510.10504

Oriented Category Theory
We develop foundations for oriented category theory, an extension of $(\infty,\infty)$-category theory obtained by systematic usage of the Gray tensor product, in order to study lax phenomena in higher category theory. As categorical dimension increases, classical category-theoretic concepts generally prove too rigid or fully break down and must be replaced by oriented versions, which allow more flexible notions of naturality and coherence. Oriented category theory provides a framework to addre…

Open-source Stand-Alone Versatile Tensor Accelerator
Anthony Faure-Gignoux, Kevin Delmas, Adrien Gauffriau, Claire Pagetti
https://arxiv.org/abs/2509.19790 https://

Open-source Stand-Alone Versatile Tensor Accelerator
Machine Learning (ML) applications demand significant computational resources, posing challenges for safety-critical domains like aeronautics. The Versatile Tensor Accelerator (VTA) is a promising FPGA-based solution, but its adoption was hindered by its dependency on the TVM compiler and by other code non-compliant with certification requirements. This paper presents an open-source, standalone Python compiler pipeline for the VTA, developed from scratch and designed with certification requirem…

Scalable Quantum Monte Carlo Method for Polariton Chemistry via Mixed Block Sparsity and Tensor Hypercontraction Method
Yu Zhang
https://arxiv.org/abs/2510.11634 https://…

Scalable Quantum Monte Carlo Method for Polariton Chemistry via Mixed Block Sparsity and Tensor Hypercontraction Method
We present a reduced-scaling auxiliary-field quantum Monte Carlo (AFQMC) framework designed for large molecular systems and ensembles, with or without coupling to optical cavities. Our approach leverages the natural block sparsity of Cholesky decomposition (CD) of electron repulsion integrals in molecular ensembles and employs tensor hypercontraction (THC) to efficiently compress low-rank Cholesky blocks. By representing the Cholesky vectors in a mixed format, keeping high-rank blocks in block-…

X-mas comes early this year: the ICFP 2025 talks are now on YouTube.
You can catch my talk on "Domain-specific tensor languages" below. We explore implementing tensor calculus in #Haskell, supporting both Einstein notation and Penrose diagrams to model things like General Relativity and black holes. 🕳️🚀
My talk:

A (very) simple proof of the gravitational energy formula of polytropic spheres
Luca Ciotti (Dept. of Physics,Astronomy, Bologna University, Italy)
https://arxiv.org/abs/2510.11882

A (very) simple proof of the gravitational energy formula of polytropic spheres
It is shown how the well-known formula for the gravitational energy of self-gravitating regular polytropes of finite mass can be obtained in an elementary way by using Gauss's divergence theorem and the Chandrasekhar virial tensor, without resorting to lengthy algebra, to specific properties of Lane-Emden functions, and to thermodynamics arguments, as is instead commonly found in standard treatises and in astrophysical literature. The present approach, due to its simplicity, can be particularly…

Koszul dual $\mathcal{A}_{\infty}$-algebras from star-shaped diagrams -- part 2
Isabella Khan
https://arxiv.org/abs/2510.12575 https://arxiv.org/pdf/2510.1…

Koszul dual $\mathcal{A}_{\infty}$-algebras from star-shaped diagrams -- part 2
This paper proves a Koszul duality result between weighted $\mathcal{A}_{\infty}$-algebras constructed in the author's previous work. In the process, we construct a new box tensor product for weighted $\mathcal{A}_{\infty}$ bimodules, and verify a correspondence between weighted $\mathcal{A}_{\infty}$-algebra maps and a particular class of $\mathcal{A}_{\infty}$-bimodule. This paper is part 2 of arXiv:2408.01564.

Category Theory for Supercomputing: The Tensor Product of Linear BSP Algorithms
Thomas Koopman, Rob H. Bisseling, Sven-Bodo Scholz
https://arxiv.org/abs/2510.00979 https://

Category Theory for Supercomputing: The Tensor Product of Linear BSP Algorithms
We show that a particular class of parallel algorithm for linear functions can be straightforwardly generalized to a parallel algorithm of their tensor product. The central idea is to take a model of parallel algorithms -- Bulk Synchronous Parallel (BSP) -- that decomposes parallel algorithms into so-called supersteps that are one of two types: a computation superstep that only does local computations, or a communication superstep that only communicates between processors. We connect each type …

just in case anyone is interested in how to do complex linear algebra, including complex tensor products, without using complex numbers - using real linear operators \(J\) with \(J\circ J=-I\) instead of complex scalars - here are some links.  
The TLDR version on a poster:
http://mwaa.math.indianapolis.iu.ed…

Tensor rank of the determinant and periodic triangulations of $\mathbb{R}^n$
Sergey Avvakumov, Roman Karasev
https://arxiv.org/abs/2509.22333 https://arxiv…

Tensor rank of the determinant and periodic triangulations of $\mathbb{R}^n$
We prove that in any $\mathbb{Z}^n$-periodic triangulation of $\mathbb{R}^n$ the number of $\mathbb{Z}^n$-orbits of $n$-dimensional simplices is at least the tensor rank of the $n$th determinant tensor. The latter is known to be at least $\frac{n^{n-1}}{(n-1)!}$, which is approximately $\frac{e^n}{\sqrt{2πn}}$ for large $n$. The triangulation is not assumed to be geometric, meaning that its simplices can be ``curved''. We also provide lower bounds for general spaces. A simplicial cell comple…

Generalized Nonnegative Structured Kruskal Tensor Regression
Xinjue Wang, Esa Ollila, Sergiy A. Vorobyov, Ammar Mian
https://arxiv.org/abs/2509.19900 https://

Generalized Nonnegative Structured Kruskal Tensor Regression
This paper introduces Generalized Nonnegative Structured Kruskal Tensor Regression (NS-KTR), a novel tensor regression framework that enhances interpretability and performance through mode-specific hybrid regularization and nonnegativity constraints. Our approach accommodates both linear and logistic regression formulations for diverse response variables while addressing the structural heterogeneity inherent in multidimensional tensor data. We integrate fused LASSO, total variation, and ridge r…

Suppression of scalar perturbations due to a heavy axion
Kai-Ge Zhang, Jian-Feng He, Chengjie Fu, Zong-Kuan Guo
https://arxiv.org/abs/2510.02828 https://ar…

Suppression of scalar perturbations due to a heavy axion
A fast-rolling axion can transfer its kinetic energy to gauge fields via the Chern-Simons coupling, leading to copious production of gauge quanta during inflation. The amplified gauge fields act as a source for both scalar and tensor perturbations. In this work, we propose a mechanism for suppressing scalar perturbations while sourcing strong tensor perturbations. We present an implementation of such a mechanism, demonstrating that sourced tensor perturbations are expected to be detected by upc…

Google says Ironwood, its seventh-gen TPU, will launch in the coming weeks and is more than 4x faster than its sixth-gen TPU; it comes in a 9,216-chip config (CNBC)
https://www.cnbc.com/2025/11/06/google-unveils-ironwood-seventh-gener…

Google's rolling out its most powerful AI chip, taking aim at Nvidia with custom silicon
Google says Ironwood, the seventh generation of its Tensor Processing Unit, is more than four times faster than its predecessor chip.

Crosslisted article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- Tensor Logic: The Language of AI
Pedro Domingos
https://ar…

Tensor time series change-point detection in cryptocurrency network data
Andreas Anastasiou, Ivor Cribben
https://arxiv.org/abs/2510.06211 https://arxiv.or…

Tensor time series change-point detection in cryptocurrency network data
Financial fraud has been growing exponentially in recent years. The rise of cryptocurrencies as an investment asset has simultaneously seen a parallel growth in cryptocurrency scams. To detect possible cryptocurrency fraud, and in particular market manipulation, previous research focused on the detection of changes in the network of trades; however, market manipulators are now trading across multiple cryptocurrency platforms, making their detection more difficult. Hence, it is important to cons…

Stochastic Gradient Descent for Incomplete Tensor Linear Systems
Anna Ma, Deanna Needell, Alexander Xue
https://arxiv.org/abs/2510.07630 https://arxiv.org/…

Stochastic Gradient Descent for Incomplete Tensor Linear Systems
Solving large tensor linear systems poses significant challenges due to the high volume of data stored, and it only becomes more challenging when some of the data is missing. Recently, Ma et al. showed that this problem can be tackled using a stochastic gradient descent-based method, assuming that the missing data follows a uniform missing pattern. We adapt the technique by modifying the update direction, showing that the method is applicable under other missing data models. We prove convergenc…

Unified Dark Matter and Dark Energy in a model of Non-Canonical Scalar-Tensor Theory
Nihal Jalal Pullisseri, Sanil Unnikrishnan
https://arxiv.org/abs/2510.07103 https://

Unified Dark Matter and Dark Energy in a model of Non-Canonical Scalar-Tensor Theory
We consider a model of non-canonical scalar-tensor theory in which the kinetic term in the Brans-Dicke action is replaced by a non-canonical scalar field Lagrangian $\mathcal{L}(X, ϕ)= λX^αϕ^β- V(ϕ)$ where $X = (1/2) \partial_μ ϕ\partial^μ ϕ$ and $α$, $β$ and $λ$ are parameters of the model. This can be considered as a simple non-canonical generalization of the Brans-Dicke theory with a potential term which corresponds to a special case of this model with the values of the paramete…

Quantized Dirac Fields in torsionful gravity: cosmological implications and links with the dark universe
Antonio Capolupo, Sante Carloni, Luca Fabbri, Simone Monda, Aniello Quaranta, Stefano Vignolo
https://arxiv.org/abs/2510.06874

Quantized Dirac Fields in torsionful gravity: cosmological implications and links with the dark universe
We consider a classical field in square torsion theory as a source of torsion for a quantum fermion field in FLRW metric. In the framework of QFT, we obtain vacuum contributions to the energy-momentum tensor and to the axial current that modify the dynamics of the classical field and the field equations as back-reaction. These contributions lead to a modified classical field and therefore to a modified torsion term $L^μ$ and expectation value of energy-momentum tensor $T^{μν}$ on the quantum…

Finite temperature dopant-induced spin reorganization explored via tensor networks in the two-dimensional $t$-$J$ model
Yintati Zhang, Aritra Sinha, Marek M. Rams, Jacek Dziarmaga
https://arxiv.org/abs/2510.04756

Finite temperature dopant-induced spin reorganization explored via tensor networks in the two-dimensional $t$-$J$ model
Doped Mott insulators host intertwined spin-charge phenomena that evolve with temperature and can culminate in stripe order or superconductivity at low temperatures. The two-dimensional $t$-$J$ model captures this interplay yet finite-temperature, infinite-size calculations remain difficult. Using purification represented by a tensor network - an infinite projected entangled-pair state (iPEPS) ansatz - we simulate the $t$-$J$ model at finite temperature directly in the thermodynamic limit, reac…

The logic of quantum mechanics
Eric Buffenoir
https://arxiv.org/abs/2510.12502 https://arxiv.org/pdf/2510.12502

The logic of quantum mechanics
The quantum logic program originated in a 1936 article by G. Birkhoff and J. von Neumann. This program is generally disregarded due to no-go theorems restricting the existence of the tensor product of elementary quantum logics and, above all, the impossibility of considering entangled states and Bell non-local states within the framework of these composite quantum logics. We revisit this study from the beginning and reverse the perspective. Here, the existence of a tensor product and a star inv…

A Kokkos-Accelerated Moment Tensor Potential Implementation for LAMMPS
Zijian Meng, Karim Zongo, Edmanuel Torres, Christopher Maxwell, Ryan Eric Grant, Laurent Karim B\'eland
https://arxiv.org/abs/2510.00193

A Kokkos-Accelerated Moment Tensor Potential Implementation for LAMMPS
We present a Kokkos-accelerated implementation of the Moment Tensor Potential (MTP) for LAMMPS, designed to improve both computational performance and portability across CPUs and GPUs. This package introduces an optimized CPU variant--achieving up to 2x speedups over existing implementations--and two new GPU variants: a thread-parallel version for large-scale simulations and a block-parallel version optimized for smaller systems. It supports three core functionalities: standard inference, confi…

Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks
Luca Bergamin, Giovanna Maria Dimitri, Fabio Aiolli
https://arxiv.org/abs/2509.22399

Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks
Semantic segmentation is a fundamental task in medical image analysis, aiding medical decision-making by helping radiologists distinguish objects in an image. Research in this field has been driven by deep learning applications, which have the potential to scale these systems even in the presence of noise and artifacts. However, these systems are not yet perfected. We argue that performance can be improved by incorporating common medical knowledge into the segmentation model's loss function. To…

Equivariance by Local Canonicalization: A Matter of Representation
Gerrit Gerhartz, Peter Lippmann, Fred A. Hamprecht
https://arxiv.org/abs/2509.26499 https://

Equivariance by Local Canonicalization: A Matter of Representation
Equivariant neural networks offer strong inductive biases for learning from molecular and geometric data but often rely on specialized, computationally expensive tensor operations. We present a framework to transfers existing tensor field networks into the more efficient local canonicalization paradigm, preserving equivariance while significantly improving the runtime. Within this framework, we systematically compare different equivariant representations in terms of theoretical complexity, empi…

Tensor Network Markov Chain Monte Carlo: Efficient Sampling of Three-Dimensional Spin Glasses and Beyond
Tao Chen, Jing Liu, Youjin Deng, Pan Zhang
https://arxiv.org/abs/2509.23945

Tensor Network Markov Chain Monte Carlo: Efficient Sampling of Three-Dimensional Spin Glasses and Beyond
Sampling the three-dimensional (3D) spin glass -- i.e., generating equilibrium configurations of a 3D lattice with quenched random couplings -- is widely regarded as one of the central and long-standing open problems in statistical physics. The rugged energy landscape, pronounced critical slowing down, and intrinsic ergodicity breaking render standard Monte Carlo methods severely inefficient, particularly for large systems at low temperatures. In this work, we introduce the Tensor Network Marko…

Congestion bounds via Laplacian eigenvalues and their application to tensor networks with arbitrary geometry
Sayan Mukherjee, Shinichiro Akiyama
https://arxiv.org/abs/2510.02725

Congestion bounds via Laplacian eigenvalues and their application to tensor networks with arbitrary geometry
Embedding the vertices of arbitrary graphs into trees while minimizing some measure of overlap is an important problem with applications in computer science and physics. In this work, we consider the problem of bijectively embedding the vertices of an $n$-vertex graph $G$ into the leaves of an $n$-leaf rooted binary tree $\mathcal{B}$. The congestion of such an embedding is given by the largest size of the cut induced by the two components obtained by deleting any vertex of $\mathcal{B}$. The c…

Monoidal model structures over infinite groups
Ioannis Emmanouil, Olympia Talelli
https://arxiv.org/abs/2510.08195 https://arxiv.org/pdf/2510.08195

Monoidal model structures over infinite groups
The stable category of modules over the algebra of a finite group with coefficients in a field is a compactly generated tensor triangulated category, that has been studied extensively in representation theory. In this paper, we provide a plethora of infinite groups G, for which the category of kG-modules (where k is a commutative coherent ring of finite global dimension) admits a monoidal model structure, in the sense of Hovey, whose associated homotopy category is a compactly generated tensor …

Pointed Hopf algebras revisited, with a view from tensor categories
Iv\'an Angiono
https://arxiv.org/abs/2510.03124 https://arxiv.org/pdf/2510.03124

Pointed Hopf algebras revisited, with a view from tensor categories
Hopf algebras appear in connection with various problems in Pure Mathematics and Theoretical Physics, mainly through their categoriesof representations, which are examples of tensor categories. In recent years, there have been major advances in the classification of finite-dimensional Hopf algebras over the complex numbers, especially when restricted to the pointed case. In this survey, we aim to review the main results in this direction, stating the classification theorems recently obtained an…

Beyond Belief Propagation: Cluster-Corrected Tensor Network Contraction with Exponential Convergence
Siddhant Midha, Yifan F. Zhang
https://arxiv.org/abs/2510.02290 https://

Beyond Belief Propagation: Cluster-Corrected Tensor Network Contraction with Exponential Convergence
Tensor network contraction on arbitrary graphs is a fundamental computational challenge with applications ranging from quantum simulation to error correction. While belief propagation (BP) provides a powerful approximation algorithm for this task, its accuracy limitations are poorly understood and systematic improvements remain elusive. Here, we develop a rigorous theoretical framework for BP in tensor networks, leveraging insights from statistical mechanics to devise a \emph{cluster expansion}…

Symmetric Persistent Tensors and their Hessian
Masoud Gharahi, Giorgio Ottaviani
https://arxiv.org/abs/2510.07404 https://arxiv.org/pdf/2510.07404

Symmetric Persistent Tensors and their Hessian
Persistent tensors, introduced in [Quantum 8 (2024), 1238], and inspired by quantum information theory, form a recursively defined class of tensors that remain stable under the substitution method and thereby yield nontrivial lower bounds on tensor rank. In this work, we investigate the symmetric case-namely, symmetric persistent tensors, or equivalently, persistent polynomials. We establish that a symmetric tensor in $\mathrm{Sym}^n \mathbb{C}^d$ is persistent if the determinant of its Hessian…

Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network
Jinshuo Zhang, Yafei Wang, Xinping Yi, Wenjin Wang, Shi Jin, Symeon Chatzinotas, Bj\"orn Ottersten
https://arxiv.org/abs/2510.02108

Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network
Although symbol-level precoding (SLP) based on constructive interference (CI) exploitation offers performance gains, its high complexity remains a bottleneck. This paper addresses this challenge with an end-to-end deep learning (DL) framework with low inference complexity that leverages the structure of the optimal SLP solution in the closed-form and its inherent tensor equivariance (TE), where TE denotes that a permutation of the input induces the corresponding permutation of the output. Build…

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs
Yifan Zhao, Egan Johnson, Prasanth Chatarasi, Vikram Adve, Sasa Misailovic
https://arxiv.org/abs/2510.08726

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs
Operator fusion has become a key optimization for deep learning, which combines multiple deep learning operators to improve data reuse and reduce global memory transfers. However, existing tensor compilers struggle to fuse complex reduction computations involving loop-carried dependencies, such as attention mechanisms. The paper introduces Neptune, a tensor compiler for advanced operator fusion for sequences of reduction operators. Neptune presents a new approach for advanced operator fusion,…

Tensor Spin Polarization Induced by Curved Freeze-Out Hypersurface
Zhong-Hua Zhang, Xu-Guang Huang
https://arxiv.org/abs/2509.20200 https://arxiv.org/pdf/2…

Tensor Spin Polarization Induced by Curved Freeze-Out Hypersurface
We investigate how the curvature of the freeze-out hypersurface polarizes massive vector bosons in relativistic heavy-ion collisions. Starting from the Proca Lagrangian and using the Wigner function formalism, we perform a systematic gradient expansion to obtain a covariant spin-polarization tensor expressed in terms of hydrodynamic fields and the curvature tensor of the freeze-out hypersurface. Analytic results for Bjorken and Gubser flows show that curvature anisotropy generates a non-zero te…

Q&A with Lyft CEO David Risher on the company's turnaround, AI, paying drivers more, Uber, partnership with Tensor for autonomous vehicle tech, and more (Nilay Patel/The Verge)
https://www.theverge.com/podcast/811532/lyft-uber-david-risher-interv…

Lyft CEO David Risher on paying drivers more and the shift to robotaxis
How Lyft is thinking about autonomy in the AI era, and why service is the most integral rideshare component.

Mixed-precision ab initio tensor network state methods adapted for NVIDIA Blackwell technology via emulated FP64 arithmetic
Cole Brower, Samuel Rodriguez Bernabeu, Jeff Hammond, John Gunnels, Sotiris S. Xanthea, Martin Ganahl, Andor Menczer, \"Ors Legeza
https://arxiv.org/abs/2510.04795

Mixed-precision ab initio tensor network state methods adapted for NVIDIA Blackwell technology via emulated FP64 arithmetic
We report cutting-edge performance results via mixed-precision spin adapted ab initio Density Matrix Renormalization Group (DMRG) electronic structure calculations utilizing the Ozaki scheme for emulating FP64 arithmetic through the use of fixed-point compute resources. By approximating the underlying matrix and tensor algebra with operations on a modest number of fixed-point representatives (``slices''), we demonstrate on smaller benchmark systems and for the active compounds of the FeMoco and…

Block-Encoding Tensor Networks and QUBO Embeddings
Sebastian Issel
https://arxiv.org/abs/2510.00935 https://arxiv.org/pdf/2510.00935

Block-Encoding Tensor Networks and QUBO Embeddings
We give an algorithm that converts any tensor network (TN) into a sequence of local unitaries whose composition block-encodes the network contraction, suitable for Quantum Eigenvalue / Singularvalue Transformation (QET/QSVT). The construction embeds each TN as a local isometry and dilates it to a unitary. Performing this step for every site of the tensor, allows the full network to be block-encoded. The theory is agnostic to virtual-bond sizes; for qubit resource counts and examples we assume g…

One-loop Euler-Heisenberg action in Lorentz-violating QED revisited
R. Ara\'ujo, T. Mariz, J. R. Nascimento, A. Yu. Petrov
https://arxiv.org/abs/2510.04252 https://

One-loop Euler-Heisenberg action in Lorentz-violating QED revisited
We discuss applications of the proper-time method in a Lorentz-violating extension of QED characterized by the modification of the mass sector through the addition of the term proportional to the antisymmetric tensor $H_{μν}$. Unlike other LV extensions of QED, in our case the one-loop Euler-Heisenberg-like action turns out to include only odd degrees of the stress tensor $F_{μν}$. Our result is shown to be UV finite, and it is confirmed using the Feynman diagrams framework.

Automatic Structure Identification for Highly Nonlinear MIMO Volterra Tensor Networks
Eva Memmel, Kim Batselier
https://arxiv.org/abs/2509.19627 https://ar…

Automatic Structure Identification for Highly Nonlinear MIMO Volterra Tensor Networks
The Volterra Tensor Network lifts the curse of dimensionality for truncated, discrete times Volterra models, enabling scalable representation of highly nonlinear system. This scalability comes at the cost of introducing randomness through initialization, and leaves open the challenge of how to efficiently determine the hyperparameters model order and memory length. In this paper, we present a unified framework that simultaneously addresses both challenges: We derive two algorithms that incremen…

Transition Matrices between Plethystic Bases of Polysymmetric Functions via Bijective Methods
Aditya Khanna
https://arxiv.org/abs/2510.12723 https://arxiv.…

Transition Matrices between Plethystic Bases of Polysymmetric Functions via Bijective Methods
Many identities involving symmetric functions can be proved through bijective manipulations of tableaux. In this paper, we prove identities involving polysymmetric functions through bijections and sign-reversing involutions. In their paper titled "Polysymmetric functions and motivic measures of configuration spaces", Asvin G and Andrew O'Desky introduced the algebra of polysymmetric functions (PSym) which can be defined as the tensor product of copies of the symmetric functions algebra (Sym) wh…

Assessment of the $a_{2}(1320)$ tensor-meson contribution to $\eta/\eta^{\prime}\to\pi^{0}\gamma\gamma$ decays
Rafel Escribano, Sergi Gonz\`alez-Sol\'is, Emilio Royo
https://arxiv.org/abs/2510.00787

Assessment of the $a_{2}(1320)$ tensor-meson contribution to $η/η^{\prime}\toπ^{0}γγ$ decays
In light of the recent measurement of the $η\toπ^{0}γγ$ decay by the KLOE-2 Collaboration, a previous analysis including vector- and scalar-meson exchange contributions using the VMD and L$σ$M frameworks, respectively, is extended in the present study to incorporate the effects of the $a_{2}(1320)$ tensor meson within a chiral context. Although the individual contribution of the $a_{2}$ is negligible, its destructive interference with the vector-meson resonances is found to be significant,…

TeMFpy: a Python library for converting fermionic mean-field states into tensor networks
Simon H. Hille, Attila Szab\'o
https://arxiv.org/abs/2510.05227 https://

TeMFpy: a Python library for converting fermionic mean-field states into tensor networks
We introduce TeMFpy, a Python library for converting fermionic mean-field states to finite or infinite matrix product state (MPS) form. TeMFpy includes new, efficient, and easy-to-understand algorithms for both Slater determinants and Pfaffian states. Together with Gutzwiller projection, these also allow the user to build variational wave functions for various strongly correlated electron systems, such as quantum spin liquids. We present all implemented algorithms in detail and describe how the…

Jacobian Algebras of Species with Potentials and 2-Representation Finite Algebras
Christoffer S\"oderberg
https://arxiv.org/abs/2510.04865 https://arx…

Jacobian Algebras of Species with Potentials and 2-Representation Finite Algebras
We study $2$-representation finite $\mathbb{K}$-algebras obtained from tensor products of tensor algebras of species. In earlier work we computed the higher preprojective algebra of said algebras to be given as Jacobian algebras of certain species with potential $(S, W)$, which are self-injective and finite dimensional. Truncating these Jacobian algebras yields a rich source of $2$-representation finite $\mathbb{K}$-algebras. Under suitable assumptions, we prove that the set of all cuts of $(S,…

On the representation type of a finite tensor category
Petter Andreas Bergh, Karin Erdmann, Julia Yael Plavnik, Sarah Witherspoon
https://arxiv.org/abs/2509.20853 https://

On the representation type of a finite tensor category
Given an exact module category over a finite tensor category with finitely generated cohomology, we show that if there exists an object of complexity at least three, then the category is of wild representation type. In particular, if the Krull dimension of the cohomology ring of the finite tensor category is at least three, then this category is wild.

Time-marching multi-level variational multiscale tensor decomposition algorithm for heat conduction with moving heat source
Xinyi Guan, Jiayi Hu, Lei Zhang, Shaoqiang Tang, Wing Kam Liu
https://arxiv.org/abs/2510.00516

Time-marching multi-level variational multiscale tensor decomposition algorithm for heat conduction with moving heat source
In this paper, we propose a time-marching multi-level Variational Multiscale-Tensor Decomposition (VMS-TD) algorithm to solve the heat equation with a moving heat source model that arises from additive manufacturing. First, we take a second-order centered difference for time semi-discretization. The temperature field is decomposed according to multiple space resolution levels, each represented by the TD method. Then we adopt the VMS formulation [T.J.R. Hughes, G.R. Feijoo, L. Mazzei, J.B. Quinc…

Vacuum Fluctuations and the Renormalized Stress-Energy Tensor on a Cone with Arbitrary Boundary Conditions
Jo\~ao Paulo M. Pitelli, Ricardo A. Mosna, Victor Hugo M. Ramos, Jo\~ao C. A. Barata
https://arxiv.org/abs/2509.20231

Vacuum Fluctuations and the Renormalized Stress-Energy Tensor on a Cone with Arbitrary Boundary Conditions
We analyze the vacuum fluctuations and the stress-energy tensor of a scalar field of mass $M$ in a conical spacetime, where the topological singularity at the apex requires boundary conditions for the field equation. The necessity of boundary conditions was established by Kay and Studer in the early 1990s, but the consequences of their arbitrariness, represented here by a parameter $q$, for renormalized observables have not been examined. While for $M=0$ stability is achieved only under Dirichl…

Scalar-Tensor Symmetric Teleparallel Gravity: Reconstruct the Cosmological History with a Steep Potential
Ghulam Murtaza, Avik De, Andronikos Paliathanasis
https://arxiv.org/abs/2510.03042

Scalar-Tensor Symmetric Teleparallel Gravity: Reconstruct the Cosmological History with a Steep Potential
Within the framework of scalar-non-metricity gravity, we introduce a steep potential together with a power-law coupling function and investigate whether the acceleration phases of the universe can be consistently described by this model. In the symmetric teleparallel formulation, and under a Friedmann--Lemaître--Robertson--Walker background, three distinct branches of the connection arise, leading to three different cosmological scenarios. We perform a detailed dynamical analysis of these mode…

Apparent Lorentz violation from disformally coupled ultralight dark matter
Guillem Dom\`enech, Apostolos Tsabodimos, Nathaniel Sherrill, Alexander Ganz, Fiona Kirk
https://arxiv.org/abs/2510.07490

Apparent Lorentz violation from disformally coupled ultralight dark matter
We study the impact of general disformal metric transformations on fermions, which shift the gravitational metric by an additional rank-2 tensor. This tensor can in principle be constructed from scalar-field gradients, vector fields, or field-strength contractions. We show this transformation results in the conventional Dirac action being modified by additional kinetic and axial-current couplings that are quadratic in the shifted field. When the field sourcing the metric shift takes on a non-tr…

$C_{1}$-cofiniteness and vertex tensor categories
Yi-Zhi Huang
https://arxiv.org/abs/2509.20737 https://arxiv.org/pdf/2509.20737

$C_{1}$-cofiniteness and vertex tensor categories
We first generalize the logarithmic tensor category theory of Huang-Lepowsky-Zhang to the more general case that the module category for a vertex operator algebra $V$ (more generally a Möbius vertex algebra) might not be closed under the contragredient functor. Then by verifying the assumptions to use this generalization, we obtain that (logarithmic) intertwining operators among $C_{1}$-cofinite grading-restricted generalized $V$-modules satisfy the associativity property (operator product exp…

Black Hole Ringdown Amplitudescopy
Francesco Crescimbeni, Xisco Jimenez-Forteza, Paolo Pani
https://arxiv.org/abs/2510.11782 https://arxiv.org/pdf/2510.117…

Black Hole Ringdown Amplitudescopy
Black hole ringdowns in extensions of General Relativity (GR) generically exhibit two distinct signatures: (1) theory-dependent shifts in the standard black-hole quasinormal modes, and (2) additional modes arising from extra fundamental fields --such as scalar, vector, or tensor degrees of freedom-- that can also contribute to the gravitational-wave signal. As recently argued, in general both effects are present simultaneously, and accurately modeling them is essential for robust tests of GR in…

An improved perturbative QCD study of the decays $B_c^ \to \eta_c L^ $
Wen-Jing Zhang, Xin Liu
https://arxiv.org/abs/2510.12216 https://arxiv.org/pdf/2510…

An improved perturbative QCD study of the decays $B_c^+ \to η_c L^+$
We perform an improved perturbative QCD study of the decays $B_c^+ \to η_c L^+$ ($L$ denotes the light ground state pseudoscalar, vector mesons and the corresponding $p$-wave scalar, axial-vector, and tensor ones) and predict their branching ratios (BRs) associated with relative ratios at leading order in the strong coupling $α_s$. Our results ${\rm BR}(B_c^+ \to η_c π^+) =(2.03^{+0.53}_{-0.41}) \times 10^{-3}$ and ${\rm BR}(B_c^+ \to η_c π^+)/{\rm BR}(B_c^+ \to J/ψπ^+) = 1.74^{+0.66}_{…

Weyl symmetry without the traceless condition
Israel Quiros
https://arxiv.org/abs/2510.09957 https://arxiv.org/pdf/2510.09957

Weyl symmetry without the traceless condition
We show that the requirement that the trace of the stress-energy tensor of matter must vanish if invariance under Weyl rescalings is a symmetry of a given gravitational theory, is not a general result but depends on the gravitational theory and on whether the matter action is form-invariant under Weyl rescalings or not. We focus on conformal general relativity with a Weyl form-invariant matter action. In this case, a linear dependence of the variations of the metric and of the Brans-Dicke scala…

The charge-singlet measurement toolbox
Abhijit Chakraborty, Randy Lewis, Christine A. Muschik
https://arxiv.org/abs/2510.08718 https://arxiv.org/pdf/2510.0…

The charge-singlet measurement toolbox
Symmetry is fundamental to physical laws across different scales$\unicode{x2014}$from spacetime structure in general relativity to particle interactions in quantum field theory. Local symmetries, described by gauge theories, are central to phenomena such as superconductivity, topological phases, and the Standard Model of particle physics. Emerging simulation techniques using tensor network states or quantum computers offer exciting new possibilities of exploring the physics of these gauge theor…