December 2025 arXiv papers — page 35

Priyanka Priyanka, Cristiano Nisoli, Yair Shokef

Complex behaviors often sit at a critical threshold between order and disorder. But not all disorder is created equal. Disorder can be trivial or constrained, and correlated disorder can even be topological. Crucially, constrained disorder can harbor memory, leading to non-trivial, sequence-dependent responses to external manipulations. And yet the fascinati

Feng Qiao, Qiu-Yi Chen, Zu-Jian Ying

The experimental access to ultra-strong couplings in light-matter interactions has made the quantum phase transition (QPT) in the quantum Rabi model practically relevant, while the physics of the QPT has not yet been fully explored. The polaron picture is a method capable of analyzing in the entire coupling regime and extracting the essential physics behind

Samuele Giuli, Hasanat Hasan, Benedikt Kloss, Marius S. Frank

Simulations of quantum matter rely mainly on Kohn-Sham density functional theory (DFT), which often fails for strongly correlated systems. Quantum embedding (QE) theories address this limitation by mapping the system onto an auxiliary embedding Hamiltonian (EH) describing fragment-environment interactions, but the EH is typically large and its iterative solu

Nozomu Takahashi, Le Bin Ho, Hiroaki Matsueda

Quantum metrology plays a central role in precision sensing, where quantum enhancement of detection performance is crucial for both fundamental studies and practical applications. In this work, we derive a tight performance bound for magnetic-field sensing with a spin ensemble in the presence of dissipation. The metrological performance is quantified by the

Xinyuan Chen, Fan Li

Principal stratification is a popular framework for causal inference in the presence of an intermediate outcome. While the principal average treatment effects are the standard target of inference, they may be insufficient when interest lies in the relative ordering of potential outcomes within a principal stratum. We introduce the principal generalized causa

Takayuki Hibi, Francesco Navarra, Ayesha Asloob Qureshi, Sara Saeedi Madani

In this paper, we provide a complete description of the minimal primes of ideals generated by adjacent $2$-minors, in terms of the so-called admissible sets and associated lattice ideals. We prove that for these ideals, the properties of being unmixed, Cohen-Macaulay, level, Gorenstein, and complete intersection are equivalent. Moreover, we give a combinator

Paúl Risco Iturralde

In the article ''On the (Non) NP-Hardness of Computing Circuit Complexity'', Murray and Williams imply the PARTITION decision problem is not known to be NP-hard via $2^{n^{o(1)}}$-size AC0 reductions. In this note, we show PARTITION is NP-hard via first-order projections. Basically, we slightly modify well-known reductions from 3SAT to SUBSET-SUM and from SU

Yongyi Yang, Liu Ziyin

Many theoretical results in deep learning can be traced to symmetry or equivariance of neural networks under parameter transformations. However, existing analyses are typically problem-specific and focus on first-order consequences such as conservation laws, while the implications for second-order structure remain less understood. We develop a general equiva

Shirui Chen, Jiantao Jiao, Lillian J. Ratliff, Banghua Zhu

Masked diffusion language models (MDLMs) offer the potential for parallel token generation, but most open-source MDLMs decode fewer than 5 tokens per model forward pass even with sophisticated sampling strategies, limiting their parallel generation potential. Existing acceleration methods either rely on fixed confidence-based heuristics or use distillation-b

Slava G. Turyshev

Fundamental physics today is best defined operationally: it is the program of identifying the microscopic degrees of freedom, symmetries, and dynamical laws that (i) reproduce the Standard Model (SM) of particle physics, General Relativity (GR), and the $\Lambda$CDM cosmological model in their regimes of validity, and (ii) explain the observed phenomena that

Alan Haynes, Christopher Lutsko

Let $B_R$ denote the closed Euclidean ball of radius $R$ in the plane. In this paper we prove that, if $V$ is the set of vertices of any unit length rhombic Penrose tiling then, for $R\ge 2$, \[\#(V\cap B_R)=\pi C_P R^2 + O(R^{2/3}(\log R)^{2/3}),\] where $C_P\approx 1.231$ is a constant.

Adam N. Elmachtoub, Kumar Goutam, Roger Lederman

We describe a novel framework for discrete choice modeling and price optimization for settings where scheduled service options (often hierarchical) are offered to customers, which is applicable across many businesses including some within Amazon. In such business settings, the customers would see multiple options, often substitutable, with their features and

S. M. Mallikarjunaiah

A robust $hp$-adaptive finite element framework is presented for the investigation of static cracks in materials characterized by complex, pointwise density variations. Within such heterogeneous media, the equilibrium equation governed by the divergence of the stress tensor is reduced to a vector-valued quasilinear partial differential equation, wherein sign

Alessio Savini

Let $(\mathcal{G},\nu)$ be a $t$-discrete ergodic groupoid. Consider a finite Von Neumann algebra $\mathcal{M}$ with separable predual. We prove that every uniformly bounded measurable representation $\rho:\mathcal{G} \rightarrow \mathrm{GL}(\mathcal{M})$ into the invertible elements of $\mathcal{M}$ is similar to a unitary representation.

Vladimir Dragović, Vasilisa Shramchenko

We introduce the dynamics of Toda curves of order $N$ and derive differential equations governing this dynamics. We apply the obtained results to describe isoperiodic deformations of $N$-periodic Toda chains and periodic difference Korteweg-de Vries equation. We describe deformations of the essential spectra of $N$-periodic two-sided Jacobi matrices. We also

Nicolas B. Garnier, Maria S. Molinet, Marta C. Antonelli, Silvia M. Lobmaier

Prenatal maternal stress alters maternal-fetal heart rate coupling, as demonstrated by the Fetal Stress Index derived from bivariate phase-rectified signal averaging. Here, we extend this framework using information-theoretical measures to elucidate underlying mechanisms. In 120 third-trimester pregnancies (58 stressed, 62 control), we computed transfer entr

Hridya Dhulipala, Xiaokai Rong, Aashish Yadavally, Tien N. Nguyen

In mutation-based greybox fuzzing, generating high-quality input seeds for the initial corpus is essential for effective fuzzing. Rather than conducting separate phases for generating a large corpus and subsequently minimizing it, we propose FuzzWise which integrates them into one process to generate the optimal initial corpus of seeds (ICS). FuzzWise levera

Geoffroy Morlat, Marceau Nahon, Augustin Chartouny, Raja Chatila

Moral actions are judged not only by their outcomes but by the context in which they occur. We present COMETH (Contextual Organization of Moral Evaluation from Textual Human inputs), a framework that integrates a probabilistic context learner with LLM-based semantic abstraction and human moral evaluations to model how context shapes the acceptability of ambi

Marvin Chancán, Avijit Banerjee, George Nikolakopoulos

Planetary rover exploration is attracting renewed interest with several upcoming space missions to the Moon and Mars. However, a substantial amount of data from prior missions remain underutilized for path planning and autonomous navigation research. As a result, there is a lack of space mission-based planetary datasets, standardized benchmarks, and evaluati

Xuehui Shen, Wenqian Xue, Yixuan Wang, Warren E. Dixon

Recent advancements in Lyapunov-based Deep Neural Networks (Lb-DNNs) have demonstrated improved performance over shallow NNs and traditional adaptive control for nonlinear systems with uncertain dynamics. Existing Lb-DNNs rely on multi-layer perceptrons (MLPs), which lack interpretable insights. As a first step towards embedding interpretable insights in the

Chinmay Giridhar, Philipp Vojta, Zohar Nussinov, Gerardo Ortiz

The notion of quantum symmetry has recently been extended to include reduced-dimensional transformations and algebraic structures beyond groups. Such generalized symmetries lead to exotic phases of matter and excitations that defy Landau's original paradigm. Here, we develop an algebraic framework for systematically deriving the fusion rules of topological d

Yin Li

Large Language Models (LLMs) are widely believed to possess self-correction capabilities, yet recent studies suggest that intrinsic self-correction--where models correct their own outputs without external feedback--remains largely ineffective. In this work, we systematically decompose self-correction into three distinct sub-capabilities: error detection, err

Stefano M. Iacus, Haodong Qi, Marcello Carammia, Thomas Juneau

Forecasting conflict-related fatalities remains a central challenge in political science and policy analysis due to the sparse, bursty, and highly non-stationary nature of violence data. We introduce DynAttn, an interpretable dynamic-attention forecasting framework for high-dimensional spatio-temporal count processes. DynAttn combines rolling-window estimati

Nairouz Mrabah, Mohamed Bouguessa, Sihem Sami

Subspace clustering methods face inherent scalability limits due to the $O(n^3)$ cost (with $n$ denoting the number of data samples) of constructing full $n\times n$ affinities and performing spectral decomposition. While deep learning-based approaches improve feature extraction, they maintain this computational bottleneck through exhaustive pairwise similar

Khondoker Mirazul Mumenin, Robert Underwood, Dong Dai, Jinzhen Wang

Error-bounded lossy compression techniques have become vital for scientific data management and analytics, given the ever-increasing volume of data generated by modern scientific simulations and instruments. Nevertheless, assessing data quality post-compression remains computationally expensive due to the intensive nature of metric calculations. In this work

Bowei Zhang, E. P. S. Shellard, James R. Fergusson

Constraining inflationary models with high precision bispectra across broad parameter ranges is a challenging task, requiring intensive computations at all stages, first, predicting the primordial inflation bispectrum from quantum field theory, secondly, projecting this forward with transfer functions to the late universe and, finally, comparing with the bis

Hridya Dhulipala, Xiaokai Rong, Tien N. Nguyen

In several software development scenarios, it is desirable to detect runtime errors and exceptions in code snippets without actual execution. A typical example is to detect runtime exceptions in online code snippets before integrating them into a codebase. In this paper, we propose Cerberus, a novel predictive, execution-free coverage-guided testing framewor

N. Namvar, E. Salahinejad, A. Saberi, M. J. Baghjeghaz

Reducing the formation temperature of single-phase multioxides is one of the classic challenges in ceramic processing, including wet-chemical synthesis routes. Toward pursuing this aim for diopside (MgCaSi2O6), the merit of different sol-gel and coprecipitation processes using the related chloride precursors followed by calcination was compared from the view

Sambhabi Bose, Kevin J. McGown, Ishan Panpaliya, Natalie Welling

Let $N_d(G,X)$ denote the number of degree $d$ extensions of $\mathbb{Q}$ with Galois closure $G$ and $|\Delta_K|\leq X$. Malle's conjecture predicts an asymptotic of the form $N_d(G,X)\sim CX^{\alpha}(\log X)^\beta$. Previously, Kl\"uners proved Malle's conjecture for $G=C_2 \wr S_4$. His proof gives a power savings of $O(X^{7/8})$. We improve Kl\"uners' re

Mohammed Sayagh

AI-agents help developers in different coding tasks, such as developing new features, fixing bugs, and reviewing code. Developers can write a Github issue and assign it to an AI-agent like Copilot for implementation. Based on the issue and its related discussion, the AI-agent performs a plan for the implementation, and executes it. However, the performance o

Hang Zhou, Yuhui Zhai, Shiyu Shen, Yanfeng Ouyang

Urban Air Mobility (UAM) is an emerging application of unmanned aerial vehicles that promises to reduce travel time and alleviate congestion in urban transportation systems. As drone density increases, UAM traffic is expected to experience congestion similar to that in ground traffic. However, the fundamental characteristics of UAM traffic, particularly unde

Francisco Rodríguez, Giancarlo Bravo

Bahar and Hausmann (2025a) claim to find evidence against the hypothesis that oil sanctions on Venezuela lead to increased migration flows to the United States. We show that their findings derive from applying a nonstandard, misspecified Engle-Granger test to first differences. This specification is incorrect because cointegration tests are designed to evalu

Nathan Stringham, Fateme Hashemi Chaleshtori, Xinyuan Yan, Zhichao Xu

People use large language models (LLMs) when they should not. This is partly because they see LLMs compose poems and answer intricate questions, so they understandably, but incorrectly, assume LLMs won't stumble on basic tasks like simple arithmetic. Prior work has tried to address this by clustering instance embeddings into regions where an LLM is likely to

Junfang Luo, Mengjun Hu, Keyun Qin

Three-way decision is widely applied with rough set theory to learn classification or decision rules. The approaches dealing with complete information are well established in the literature, including the two complementary computational and conceptual formulations. The computational formulation uses equivalence relations, and the conceptual formulation uses

Jing Liu, Mengjun Hu, Guangming Lang

Most existing work on three-way conflict analysis has focused on trisecting agent pairs, agents, or issues, which contributes to understanding the nature of conflicts but falls short in addressing their resolution. Specifically, the formulation of feasible strategies, as an essential component of conflict resolution and mitigation, has received insufficient

Junfang Luo, Mengjun Hu, Guangming Lang, Xin Yang

Trisecting agents, issues, and agent pairs are essential topics of three-way conflict analysis. They have been commonly studied based on either a rating or an auxiliary function. A rating function defines the positive, negative, or neutral ratings of agents on issues. An auxiliary function defines the alliance, conflict, and neutrality relations between agen

Alejandro Cholaquidis, Ricardo Fraiman, Manuel Hernández-Banadik, Stanislav Nagy

This paper studies the problem of identifying directions of axial symmetry in multivariate distributions. Theoretical results are derived on how the measure or cardinality of the set of symmetry directions relates to spherical symmetry. The problem is framed using random projections, leading to a proof that in \(\RR^2\), agreement on two random projections i

Nicole Ticea, Elias Portoles, Eliott Rosenberg, Alexander Schuckert

The emergence of states with long-range correlations in a disordered landscape is rare, as disorder typically suppresses the particle mobility required for long-range coherence. But when more than two energy levels are available per site, disorder can induce resonances that locally enhance mobility. Here we explore phases arising from the interplay between d

Ksenia Fedosova, Kim Klinger-Logan

We find exact identities for sums of the form \begin{equation*}\label{eq:convsumabs} \sum_{\stackrel{n_1+n_2 = n}{n_1 \in \mathbb{Z} \setminus \{ 0, n \} }} Q(n_1,n_2) \sigma_{-r_1}(n_1) \sigma_{-r_2}(n_2), \end{equation*} where $n\in\mathbb{N}$, $r_1,r_2\in\mathbb{C}$, $Q$ is a combination of hypergeometric functions, and $\sigma_{a}(x)$ denotes the divisor

Alimu Alibotaiken, Suyang Wang, Oluwaseun T. Ajayi, Yu Cheng

The age of information (AoI) has become a central measure of data freshness in modern wireless systems, yet existing surveys either focus on classical AoI formulations or provide broad discussions of reinforcement learning (RL) in wireless networks without addressing freshness as a unified learning problem. Motivated by this gap, this survey examines RL spec

Derguene Mbaye, Tatiana D. P. Mbengue, Madoune R. Seye, Moussa Diallo

Natural Language Processing (NLP) is rapidly transforming research methodologies across disciplines, yet African languages remain largely underrepresented in this technological shift. This paper provides the first comprehensive overview of NLP progress and challenges for the six national languages officially recognized by the Senegalese Constitution: Wolof,

Sean Plummer

Singular learning theory characterizes Bayesian models with non-identifiable parameterizations through two central quantities: the real log canonical threshold (RLCT), which governs marginal likelihood asymptotics, and the singular fluctuation, which determines second-order generalization behavior and the complexity term in WAIC. While the geometric meaning

Johnathan Hoggarth, Pablo E. Illing, Eric R. Weeks, Kari Dalnoki-Veress

We study clogging of cohesive particles in a 2D hopper with experiments and simulations. The system consists of buoyant, monodisperse oil droplets in an aqueous solution, where the droplet size, buoyant force, cohesion, and hopper opening are varied. Stronger cohesion enhances clogging, a trend confirmed in simulations. Balancing buoyant and cohesive forces

Giacomo Turri, Grégoire Pacreau, Giacomo Meanti, Timothée Devergne

kooplearn is a machine-learning library that implements linear, kernel, and deep-learning estimators of dynamical operators and their spectral decompositions. kooplearn can model both discrete-time evolution operators (Koopman/Transfer) and continuous-time infinitesimal generators. By learning these operators, users can analyze dynamical systems via spectral

A. Murat Maga, Jean-Christophe Fillion-Robin

The digitization of biological specimens has revolutionized the field of morphology, creating large collections of 3D data, and microCT in particular. This revolution was initially supported by the development of open-source software tools, specifically the development of SlicerMorph extension to the open-source image analytics platform 3D Slicer. Through Sl

Pranav Sampathkumar, Tim Huege, Andreas Haungs, Ralph Engel

Cosmic ray shower detection using large radio arrays has gained significant traction in recent years. With massive improvements in signal modelling and microscopic simulations, the analysis of incoming events is still severely limited by the simulation cost of radio emission to interpret the data. In this work, we show that a neural network can be used for s

D. G. Nanopoulos, P. Laskos-Patkos, Ch. C. Moustakidis

Recent analyses on the central compact object in the HESS J1731-347 supernova remnant reported not only surprising structural properties (mass $M$ and radius $R$), but also an interesting thermal evolution. More precisely, it has been estimated that $M=0.77^{+0.20}_{-0.17}M_\odot$ and $R=10.4^{+0.86}_{-0.78}$ km (at the $1\sigma$ level), while a redshited su

Fiana Jacobzon

In this work we consider a family of function classes constructed by means of the Gauss hypergeometric function $_2F_1(1,1;2;z) =-\frac{\log(1-z)}{z}$. We demonstrate that this family, in fact, constitutes classes of analytic functions subject to prescribed constraints on their derivatives. For these classes we obtain some geometric characteristics, includin

Gaurav Gawade

We compare low-redshift ($z<0.1$) BPT-selected pure optical AGN hosts in SDSS DR7 to colour-selected "green-valley" analogue central galaxies in IllustrisTNG100 and EAGLE Ref-L0100N1504. To reduce cross-dataset systematics, we define the green valley internally using $(g-r)$ percentiles: for galaxies with $\log_{10}(M_\star/M_\odot)>10$, we select the 75th-9

Tianwei Lan, Farid Naït-Abdesselam

The rapid growth in both the scale and complexity of Android malware has driven the widespread adoption of machine learning (ML) techniques for scalable and accurate malware detection. Despite their effectiveness, these models remain vulnerable to adversarial attacks that introduce carefully crafted feature-level perturbations to evade detection while preser

Sen Zhang, Yipie Liu, Brian Mark, Weiwen Jiang

The proposed framework represents the first tool to compile a quantum circuit across photonic-connected distributed quantum processors. Its design follows a divide-and-conquer paradigm for circuit partitioning, transpilation, and assembly, producing simulable and implementable circuit layouts.

Arnav Gupta, Gurekas Singh Sahney, Hardik Rathi, Abhishek Chandwani

Evaluating short-form video content requires moving beyond surface-level quality metrics toward human-aligned, multimodal reasoning. While existing frameworks like VideoScore-2 assess visual and semantic fidelity, they do not capture how specific audiovisual attributes drive real audience engagement. In this work, we propose a data-driven evaluation framewor

Bruce E. Sagan, Chenchen Zhao

Let u be a word over the positive integers P. Motivated by a question involving crystal graphs, Sagan and Wilson initiated the study of the centralizer of u in the plactic monoid which is the set C(u) = {w | uw is Knuth equivalent to wu}. In particular, they conjectured the following stability phenomenon: for any u there is a positive integer K depending onl

B. Amghar, M. Yachi, M. Amghar, M. Almousa

We present a unified geometric and dynamical framework for a physical system consisting of $n$ spin-$1/2$ particles with all-range Ising interaction. Using the Fubini-Study formalism, we derive the metric tensor of the associated quantum state manifold and compute the corresponding Riemann curvature. Our analysis reveals that the system evolves over a smooth

Mark Dominique Dalipe Muñoz

The use of descriptive statistics in pilot testing procedures requires objective, standard diagnostic tools that are feasible for small sample sizes. While current psychometric practices report item-level statistics, they often report these raw descriptives separately rather than consolidating both mean and standard deviation into a single diagnostic tool to

Rahul Moorthy Mahesh, Oguzhan Goktug Poyrazoglu, Yukang Cao, Volkan Isler

In cluttered environments, motion planners often face a trade-off between safety and speed due to uncertainty caused by occlusions and limited sensor range. In this work, we investigate whether co-pilot instructions can help robots plan more decisively while remaining safe. We introduce PaceForecaster, as an approach that incorporates such co-pilot instructi

Aswathnarayan Muthukrishnan Kirubakaran, Adithya Parthasarathy, Nitin Saksena, Ram Sekhar Bodala

Cloud data pipelines increasingly operate under dynamic workloads, evolving schemas, cost constraints, and strict governance requirements. Despite advances in cloud-native orchestration frameworks, most production pipelines rely on static configurations and reactive operational practices, resulting in prolonged recovery times, inefficient resource utilizatio

Lena Collienne, Frederick A Matsen

Tree rearrangements such as Nearest Neighbor Interchange (NNI) and Subtree Prune and Regraft (SPR) are commonly used to explore phylogenetic treespace. Computing distances based on them, however, is often intractable, so the efficiently computable Robinson-Foulds (RF) distance is used in practice. We investigate how the RF distance behaves along paths in the

Doğukan Özbayrak, Ahmed Hareedy

In the age of data revolution, a modern storage~or transmission system typically requires different levels of protection. For example, the coding technique used to fortify data in a modern storage system when the device is fresh cannot be the same as that used when the device ages. Therefore, providing reconfigurable coding schemes and devising an effective

Natalia Espinosa-Dice, Nicholas J. Jackson, Chao Yan, Aaron Lee

Synthetic data generation (SDG) is a promising approach for enabling data sharing in biomedical studies while preserving patient privacy. Yet, state-of-the-art generative models often require large datasets and complex training procedures, limiting their applicability in small-sample settings common in biomedical research. This study aims to develop a more p

Yinying Huang, Oliver Jenkinson, Zhiqiang Li

The theory of ergodic optimization for distance-expanding maps is extended to Gauss's continued fraction map. Since the set of invariant probability measures is not weak$^*$ closed, we establish a characterisation of the closure of this set, and investigate limit-maximizing measures for H\"older continuous functions. Although a Ma\~n\'e cohomology lemma is s

Filip Broćić, Stefan Matijević

We show that the symplectic $2$-product of $n$ two-dimensional star-shaped domains has an interior symplectomorphic to that of a symplectic ellipsoid. Adapting this construction, given $0<\alpha \leq 1$, we obtain that every open subset of $\mathbb{R}^{2n}$ with a smooth boundary is symplectomorphic to an open set whose boundary contains a set of Hausdorff d

Tal Adi, Kris Pardo, Olivier Doré

We explore the sensitivity of weak lensing surveys to gravitational waves (GWs) emitted by inspiraling supermassive black hole binaries (SMBHBs) in the nanohertz to microhertz frequency band, bridging the gap between pulsar timing arrays and space-based interferometers. Building on the formalism for GW-induced shear distortions, we develop a signal-to-noise

Mohammad Hammas Saeed, Isaiah J. King, Howie Huang

Influence campaigns are a growing concern in the online spaces. Policymakers, moderators and researchers have taken various routes to fight these campaigns and make online systems safer for regular users. To this end, our paper presents ALETHEIA, a system that formalizes the detection of malicious accounts (or troll accounts) used in such operations and fore

Martin Benedikt, Ippocratis D. Saltas

We introduce GraviBERT, a novel deep learning framework for gravitational wave inference, built on a multi-scale feature extractor with a transformer encoder and a suitable regression head. A key novelty of GraviBERT is its staged training: a BERT-style self-supervised pretraining phase to learn transferable representations, followed by supervised fine-tunin

Gyeo-Re Han, Merve Eryilmaz, Artem Goncharov, Yuzhu Li

Rapid and accessible cardiac biomarker testing is essential for the timely diagnosis and risk assessment of myocardial infarction (MI) and heart failure (HF), two interrelated conditions that frequently coexist and drive recurrent hospitalizations with high mortality. However, current laboratory and point-of-care testing systems are limited by long turnaroun

Tanmoy Mondal, Pinaki Majumdar

Ferromagnetic polarons are self trapped states of an electron in a locally spin polarised environment. They occur close to the magnetic $T_c$ in low carrier density local moment magnets when the electron-spin coupling is comparable to the hopping scale. In non disordered systems the primary signatures are a modest non-monotonicity in the temperature dependen

Ali Övgün, Reggie C. Pantig, Bobomurat Ahmedov, Uktamjon Uktamov

Motivated by the work of Scully \textit{et al.} [ \textcolor{blue}{Proc. Nat. Acad. Sci. 115, 8131 (2018)}] and Camblong \textit{et al.}[ \textcolor{blue}{Phys. Rev. D 102, 085010 (2020)}], we investigate horizon-brightened acceleration radiation (HBAR) for freely falling two-level atoms in the geometry of a Bardeen regular black hole. Building on the quantu

Xiang-Gan Liu, Michael Ratz

Modular symmetries are known to be powerful and have various remarkable properties. We point out that the structure of vector-valued modular forms (VVMFs) space leads to the absence of couplings which cannot be explained in terms of the usual symmetries. These modular zeros, which correspond to gaps in spaces of VVMFs, have the power of explaining certain st

Or Katz, Alexander Schuckert, Tianyi Wang, Eleanor Crane

While quantum simulators promise to explore quantum many-body physics beyond classical computation, their capabilities are limited by the available native interactions in the hardware. On many platforms, accessible Hamiltonians are largely restricted to one- and two-body interactions, limiting access to multi-body Hamiltonians and to systems governed by simu

Ravi Kumar, Tomer Firon, André Haug, Misha Yutushui

The effective interaction between composite fermions, set entirely by the Coulomb potential and the underlying electronic Landau level orbitals, can stabilize exotic fractional quantum Hall states. In particular, half-filled Landau levels with different orbital character can host either metallic or paired phases of composite fermions. Here, we leverage exper

V. Knapp-Perez, Xiang-Gan Liu, Hans Peter Nilles, Saul Ramos-Sanchez

Effective field theories arising from string compactifications are subject to constraints originating from the duality transformations of string theory. Interpreting these so-called selection rules in terms of conventional symmetries has remained challenging. We show that particular selection rules in heterotic orbifolds can be explained from a subtle interp

Haonan Qiu, Shikun Liu, Zijian Zhou, Zhaochong An

High-resolution video generation, while crucial for digital media and film, is computationally bottlenecked by the quadratic complexity of diffusion models, making practical inference infeasible. To address this, we introduce HiStream, an efficient autoregressive framework that systematically reduces redundancy across three axes: i) Spatial Compression: deno

Li-Zhong Szu-Tu, Ting-Lin Wu, Chia-Jui Chang, He Syu

We expose a significant popularity bias in state-of-the-art vision-language models (VLMs), which achieve up to 34% higher accuracy on famous buildings compared to ordinary ones, indicating a reliance on memorization over generalizable understanding. To systematically investigate this, we introduce the largest open benchmark for this task: the YearGuessr data

Ziyu Chen, Xinbei Jiang, Peng Sun, Tao Lin

Masked Diffusion Models (MDMs) offer flexible, non-autoregressive generation, but this freedom introduces a challenge: final output quality is highly sensitive to the decoding order. We are the first to formalize this issue, attributing the variability in output quality to the cumulative predictive uncertainty along a generative path. To quantify this uncert

Jiaer Xia, Peixian Chen, Mengdan Zhang, Xing Sun

We present Streamo, a real-time streaming video LLM that serves as a general-purpose interactive assistant. Unlike existing online video models that focus narrowly on question answering or captioning, Streamo performs a broad spectrum of streaming video tasks, including real-time narration, action understanding, event captioning, temporal event grounding, an

Avilasha Mandal, Chaoning Zhang, Fachrina Dewi Puspitasari, Xudong Wang

Segment Anything Model 2 (SAM2), a vision foundation model has significantly advanced in prompt-driven video object segmentation, yet their practical deployment remains limited by the high computational and memory cost of processing dense visual tokens across time. The SAM2 pipelines typically propagate all visual tokens produced by the image encoder through

Jin Qin, Zihan Liao, Ziyin Zhang, Hang Yu

We present C2LLM - Contrastive Code Large Language Models, a family of code embedding models in both 0.5B and 7B sizes. Building upon Qwen-2.5-Coder backbones, C2LLM adopts a Pooling by Multihead Attention (PMA) module for generating sequence embedding from token embeddings, effectively 1) utilizing the LLM's causal representations acquired during pretrainin

Varun Belagali, Saarthak Kapse, Pierre Marza, Srijan Das

The interpretation of small tiles in large whole slide images (WSI) often needs a larger image context. We introduce TICON, a transformer-based tile representation contextualizer that produces rich, contextualized embeddings for ''any'' application in computational pathology. Standard tile encoder-based pipelines, which extract embeddings of tiles stripped f

Fabia F. Athena, Xiangjin Wu, Nathaniel S. Safron, Amy Siobhan McKeown-Green

As we move beyond the era of transistor miniaturization, back-end-of-line-compatible transistors that can be stacked monolithically in the third dimension promise improved performance for low-power electronics. In advanced transistor architectures, such as gate-all-around nanosheets, the conventional channel-first process involves depositing dielectrics dire

Xinhe Wang, Jin Huang, Xingjian Zhang, Tianhao Wang

Reasoning benchmarks such as the Abstraction and Reasoning Corpus (ARC) and ARC-AGI are widely used to assess progress in artificial intelligence and are often interpreted as probes of core, so-called ``fluid'' reasoning abilities. Despite their apparent simplicity for humans, these tasks remain challenging for frontier vision-language models (VLMs), a gap c

Azadeh Maleknejad

Gravitational waves provide a unique window into gravity, cosmology, and high-energy physics, enabling the exploration of fundamental phenomena across a wide range of scales. This review presents a coherent and pedagogical framework that bridges foundational theory with observational frontiers. We begin by developing the theory of gravitational radiation wit

Mir Afrasiar, Jaydeep Kumar Basak, Keun-Young Kim

We study the holographic construction of timelike entanglement entropy (tEE) in black hole backgrounds in Lorentzian geometries. The holographic tEE is realized through extremal surfaces consisting of spacelike and timelike branches that encode its real and imaginary components, respectively. In the BTZ black hole, these surfaces extend into the interior of

Sida Wang

Separating signal from noise is central to experiments. Applying well-established statistical methods effectively to LLM evals requires consideration of their unique noise characteristics. We clearly define and measure three types of noise: prediction noise from generating different answers on a given question, data noise from sampling questions, and their c

Wan Ki Wong, Ka Ho To, Chuck-jee Chau, Lucas Wong

In this study, we present a novel automatic piano reduction method with semi-supervised machine learning. Piano reduction is an important music transformation process, which helps musicians and composers as a musical sketch for performances and analysis. The automation of such is a highly challenging research problem but could bring huge conveniences as manu

Felix Draxler, Justus Will, Farrin Marouf Sofian, Theofanis Karaletsos

Autoregressive decoding in language models is inherently slow, generating only one token per forward pass. We propose Parallel Token Prediction (PTP), a general-purpose framework for predicting multiple tokens in a single model call. PTP moves the source of randomness from post-hoc sampling to random input variables, making future tokens deterministic functi

Magnus H. Haaland, Anđela Šarković

We consider a random geometric graph obtained by placing a Poisson point process of intensity 1 in the d-dimensional torus of side length n^(1/d) and connecting two points by an edge if their distance is at most r. We consider the case of d>=2 and r in [r_min, r_max], where r_min<r_max are any constants with r_min>r_g and r_g is a constant above which this g

Alan A. Tedeev

We consider the Cauchy problem for the nonstationary discrete p-Laplacian with inhomogeneous density \r{ho}(x) on an infinite graph which supports the Sobolev inequality. For nonnegative solutions when p > 2, we prove the precise rate of stabilization in time, provided \r{ho}(x) is a non-power function. When p > 2 and \r{ho}(x) goes to zero fast enough, we p

Roberto Garrone

Population-scale pangenome analysis increasingly requires representations that unify single-nucleotide and structural variation while remaining scalable across large cohorts. Existing formats are typically sequence-centric, path-centric, or sample-centric, and often obscure population structure or fail to exploit carrier sparsity. We introduce the H1 pan-gra

Yuan Qiu, Wolfgang Dahmen, Peng Chen

Minimizing PDE-residual losses is a common strategy to promote physical consistency in neural operators. However, standard formulations often lack variational correctness, meaning that small residuals do not guarantee small solution errors due to the use of non-compliant norms or ad hoc penalty terms for boundary conditions. This work develops a variationall

Mohammad Hammas Saeed, Howie Huang

Cyberattacks pose a serious threat to modern sociotechnical systems, often resulting in severe technical and societal consequences. Attackers commonly target systems and infrastructure through methods such as malware, ransomware, or other forms of technical exploitation. Most traditional mechanisms to counter these threats rely on post-hoc detection and miti

Himanshu Chandrakar, Anurag Singh

The independence complex of a graph is a simplicial complex whose faces correspond to the independent sets of $G$. While independence complexes have been studied extensively for many graph classes, including square grid graphs, relatively little is known about planar hexagonal grid graphs. In this article, we study the topology of the independence complexes

Yiqi Huang, Tristan Ozuch

We refine the regularity of noncollapsed limits of 5-dimensional manifolds with bounded Ricci curvature. In particular, for noncollapsed limits of Einstein 5-manifolds, we prove that (1) tangent cones are unique of the form $\mathbb{R}\times\mathbb{R}^4/\Gamma$ on the top stratum, hence outside a countable set of points; this follows from a new isolation res

Ali Merali

This paper derives `Scaling Laws for Economic Impacts' -- empirical relationships between the training compute of Large Language Models (LLMs) and professional productivity. In a preregistered experiment, over 500 consultants, data analysts, and managers completed professional tasks using one of 13 LLMs. We find that each year of AI model progress reduced ta

Vincenzo Alba, Sanam Azarnia, Gianluca Lagnese, Federico Rottoli

We investigate the distribution of the eigenvalues of the reduced density matrix (entanglement spectrum) after a global quantum quench. We show that in an appropriate scaling limit the lower part of the entanglement spectrum exhibits ``universality''. In the scaling limit and at asymptotically long times the distribution of the entanglement spectrum depends

Sasan Sarbishegi, Maryam Sadat Mirkamali

In this paper, we present a general numerical framework for both deterministic and probabilistic quantum state transformations, under locality constraints. For a given arbitrary bipartite initial state and a desired bipartite target state, we construct an optimized local quantum channel that transforms the initial state into the target state with high fideli

Bing Hao, Minglai Shao, Zengyi Wo, Yunlong Chu

The widespread application of Large Language Models (LLMs) has motivated a growing interest in their capacity for processing dynamic graphs. Temporal motifs, as an elementary unit and important local property of dynamic graphs which can directly reflect anomalies and unique phenomena, are essential for understanding their evolutionary dynamics and structural

Guopeng Li, Ruiqi Wu, Haisheng Tan

Integrating large language models (LLMs) into personal assistants, like Xiao Ai and Blue Heart V, effectively enhances their ability to interact with humans, solve complex tasks, and manage IoT devices. Such assistants are also termed LLM-driven agents. Upon receiving user requests, the LLM-driven agent generates plans using an LLM, executes these plans thro

Clark Butler

We give a new construction of the measure of maximal entropy for transitive Anosov flows through a method analogous to the construction of Patterson-Sullivan measures in negative curvature. In order to carry out our procedure we prove several new results concerning the global geometry of the leaves of the center-unstable foliation of an Anosov flow. We show

Marcel Griesemer, Valentin Kussmaul

Asymptotic completeness of Rayleigh scattering in models of atoms and molecules of non-relativistic QED is expected, but for a proof we still lack sufficient control on the number of emitted soft photons. So far, this obstacle has only been overcome for the spin-boson model. In a general class of models asymptotic completeness holds provided the expectation