March 2026 arXiv papers — page 115

Robert Canyellas, Mikhail I. Katsnelson, Andrey Bagrov

Fractal structures such as the Sierpi\'nski gasket have been predicted to enhance the critical temperature of s-wave superconductivity compared to regular crystals while maintaining macroscopic phase coherence of Cooper pairs. Here we extend this analysis to order parameters with non-trivial symmetry by studying the extended Hubbard model with nearest-neighb

Zitong Yang

Modern language model-based AI systems are remarkably powerful, yet their capabilities remain fundamentally capped by their human creators in three key ways. First, although a model's weights can be updated via fine-tuning, acquiring new knowledge from small, specialized corpora after pretraining remains highly data-inefficient. Second, the training of these

Seyed Naseh Sajadi, Supakchai Ponglertsakul, Julio Oliva

With the aim of continuing the exploration of near-horizon charges in higher-curvature gravity, searching for sectors leading to universal behaviors, we first provide a thorough revision and formulae of the covariant phase-space method applied to arbitrary gravitational theories containing up to quartic terms in the Riemann tensor in arbitrary dimension. The

Karan Goyal, Dikshant Kukreja, Vikram Goyal, Mukesh Mohania

Proper citation of relevant literature is essential for contextualising and validating scientific contributions. While current citation recommendation systems leverage local and global textual information, they often overlook the nuances of the human citation behaviour. Recent methods that incorporate such patterns improve performance but incur high computat

Xuri Ge, Chunhao Wang, Xindi Wang, Zheyun Qin

Composed Image Retrieval (CIR) aims to retrieve target images based on a reference image and modified texts. However, existing methods often struggle to extract the correct semantic cues from the reference image that best reflect the user's intent under textual modification prompts, resulting in interference from irrelevant visual noise. In this paper, we pr

Jared James Grogan

The foundation model era -- roughly 2020 to 2025 -- is over. The forces that defined it have inverted. Open source models have reached frontier performance while inference costs approach zero, exposing what was always structurally true: pre-training large language models at scale is not a durable competitive moat. The US government's formal designation of An

Mikito Koga, Masashige Matsumoto

Crystal field quartets with quadrupole degrees of freedom play a crucial role in hidden ordering systems, as exemplified by CeB$_6$. We present a novel magnetoacoustic resonance technique that combines acoustically induced strain fields with a linearly polarized high-frequency microwave field to probe quadrupoles inherent in the quartet hidden behind magneti

James L. Gray, Nikolai Goncharov, Alexandre Cardaillac, Ryan Griffiths

Asset management requires accurate 3D models to inform the maintenance, repair, and assessment of buildings, maritime vessels, and other key structures as they age. These downstream applications rely on high-fidelity models produced from aerial surveys in close proximity to the asset, enabling operators to locate and characterise deterioration or damage and

Nathan Zhao

Computer use agents create new privacy risks: training data collected from real websites inevitably contains sensitive information, and cloud-hosted inference exposes user screenshots. Detecting personally identifiable information in web screenshots is critical for privacy-preserving deployment, but no public benchmark exists for this task. We introduce WebP

Vishva Patel, Sayantan Bhattacharya, Sudip Bhattacharyya, Pankaj S. Joshi

Theoretical studies suggest that gravitational collapse can form either a black hole or a visible (naked) singularity. Identifying observational signatures that distinguish these two types of collapsed objects is a holy grail of physics. Here, we examine whether relativistic accretion disk spectra can provide such a test. We construct an additive table model

Yiwen Zhao, Ce Zheng, Yufu Wang, Hsueh-Han Daniel Yang

Human mesh recovery (HMR) models 3D human body from monocular videos, with recent works extending it to world-coordinate human trajectory and motion reconstruction. However, most existing methods remain offline, relying on future frames or global optimization, which limits their applicability in interactive feedback and perception-action loop scenarios such

Hengyuan Zhang, Xinrong Chen, Zunhai Su, Xiao Liang

Layer-wise mixed-precision quantization (LMPQ) enables effective compression under extreme low-bit settings by allocating higher precision to sensitive layers. However, existing methods typically treat all intra-layer weight modules uniformly and rely on a single numerical property when estimating sensitivity, overlooking their distinct operational roles and

Jianquan Ge, Lixin Xiao

Addressing Yau's conjecture (Problem 117) on $S^4$, we investigate the self-duality of weakly stable Yang-Mills fields under the assumption of irreducibility. For structure groups with a simple Lie algebra, we prove that any weakly stable irreducible connection must be either self-dual or anti-self-dual. Furthermore, we demonstrate that if the Lie algebra ad

Zhongyuang Liu, Min He, Shaonan Yu, Xinhang Xu

Language-guided embodied navigation requires an agent to interpret object-referential instructions, search across multiple rooms, localize the referenced target, and execute reliable motion toward it. Existing systems remain limited in real indoor environments because narrow field-of-view sensing exposes only a partial local scene at each step, often forcing

Rodolfo Garcia, Rory Barnes, Peter E. Driscoll, Victoria S. Meadows

We simulate Venus' evolution with a coupled one-dimensional solar-atmosphere-lithosphere-mantle-core model to predict currently unobservable features and its eruptive mass flux. We identified four distinct evolutionary pathways that simultaneously match the atmospheric abundances of water and carbon dioxide as well as the lack of a core dynamo. These scenari

G. Hernández-Tomé, J. P. Hoyos-Daza, O. G. Miranda, R. Sánchez-Vélez

Charged lepton flavor violation provides a clear experimental signature in the search for physics beyond the Standard Model. In this work, we study the flavor-violating three-body tau decays $\tau^- \to \ell_i^- \ell_i^- \ell_j^+$ ($\ell_i \neq \ell_j$) induced by the scalar leptoquarks $R_2$ and $S_1$, focusing on flavor structures dominated by top- or char

Hui-Jing Zheng, Ke-Xin Pang, Yun-Yun Bai, Yanfeng Ge

The Nielsen--Ninomiya theorem requires that the total topological chiral charges in a crystal vanish, a constraint typically satisfied by identical nodes like Weyl--Weyl pairs. Whether a minimal heterogeneous configuration -- comprising a single Weyl point (WP) and a single Dirac point (DP) -- can exist in an electronic system has remained unresolved. Here,

Rongyi Dai, Jeffrey Kuan, Krutika Tawri, Sunčica Čanić

We study the one-dimensional isentropic compressible Euler equations with linear (frictional) damping, subject to multiplicative, white-in-time stochastic forcing. The system is posed on a bounded interval with $L^\infty$ initial data and Dirichlet boundary conditions imposed on the momentum. We establish the global-in-time existence of $L^\infty$ martingale

Zechang Xiong, Da Li, Kexin Tang, Pengyuan Li

Multimodal models often converge to a dominant-modality solution, in which a stronger, faster-converging modality overshadows weaker ones. This modality imbalance causes suboptimal performance. Existing methods attempt to balance different modalities by reweighting gradients or losses. However, they overlook the fact that each modality has finite information

Xiaomin Wei, Zijun Xu, Weiguo Lu, Yang Zhou

Motivated by the stringent requirements of the Upstream Pixel (UP) tracker in the LHCb Upgrade II and the Inner Tracking detector (ITK) of the Circular Electron Positron Collider, the COFFEE series of pixel sensor chips have been developed using a 55nm High-Voltage CMOS (HVCMOS) process. The primary objective is to achieve a time resolution of a few nanoseco

Chien-Chung Huang, Naonori Kakimura, Yusuke Kobayashi, Tatsuya Terao

This paper studies randomized polynomial kernelization for the weighted $d$-matroid intersection problem. While the problem is known to have a kernel of size $O(d^{(k - 1)d})$ where $k$ is the solution size, the existence of a polynomial kernel is not known, except for the cases when either all the given matroids are partition matroids~(i.e., the $d$-dimensi

Jaeseok Choi, Anand Deo, Constantino Lagoa, Anirudh Subramanyam

Chance-constrained optimization is a suitable modeling framework for safety-critical applications where violating constraints is nearly unacceptable. The scenario approach is a popular solution method for these problems, due to its straightforward implementation and ability to preserve problem structure. However, in the rare-event regime where constraint vio

Jeongbin Jo

Linear response theory and Green's functions provide a universal framework for understanding dynamical correlations in strongly correlated open quantum systems. While the theoretical foundation for non-Hermitian linear response has been recently established to describe dissipation and fluctuation-dissipation relations (FDR), generalizing these predictions on

Junichi Baba

We present a bar-informed kinematic-distance (BIKD) method to reconstruct face-on molecular-gas maps of the inner Milky Way from PPV data, relaxing the standard assumption of axisymmetric circular rotation that can generate severe artifacts in barred regions. BIKD replaces the rotation curve with a non-axisymmetric streaming field extracted from hydrodynamic

Lei Xie, Peihui Lin, Naiyu Wang, Paolo Gardoni

Urban flood emergency response increasingly relies on infrastructure impact forecasts rather than hazard variables alone. However, real-time predictions are unreliable due to biased rainfall, incomplete flood knowledge, and sparse observations. Conventional open-loop forecasting propagates impacts without adjusting the system state, causing errors during cri

Junhyeok Lee

Reference lists in scholarly manuscripts frequently contain errors, including incorrect identifiers, incomplete metadata, misattributed authors, and mismatches between preprint and published versions. These problems are tedious to repair manually and have become more visible in workflows that rely on large language models, which can fabricate or corrupt cita

Gaia Pozzoli, Renaud Raquépas

We work with infinite, closed, translation-invariant, finite-range lattice systems with "unbounded classical spins", also known as anharmonic crystals, under assumptions close to those used by Lanford, Lebowitz and Lieb (J. Stat. Phys., 1977); among other conditions, the pinning dominates the interaction. In this context, we prove conservation of the specifi

Ava Nederlander, Zainab Aamir, Arie E. Kaufman

Upcoming astronomical surveys produce imagery that spans many orders of magnitude in spatial scale, requiring scientists to reason fluidly between global structure and local detail. Data from the Vera C. Rubin Observatory exemplifies this challenge, as traditional desktop-based workflows often rely on discrete views or static cutouts that fragment context du

Alexander Lehner, Giovanni Righetto

Brain drain -- the emigration of skilled individuals toward higher-wage economies -- is a well-documented phenomenon, yet its aggregate economic cost remains difficult to quantify because individual productivity is rarely observed. We offer a novel angle on this measurement challenge by studying professional football, a global labour market in which every pa

Jayanth Bhargav, Zirui Xu, Vasileios Tzoumas, Mahsa Ghasemi

We study a target coverage problem in which a team of sensing agents, operating under limited communication, must collaboratively monitor targets that may be adaptively repositioned by an attacker. We model this interaction as a zero-sum game between the sensing team (known as the defender) and the attacker. However, computing an exact Nash equilibrium (NE)

Tianyu Zhao

Let $0<\gamma_1\leq \gamma_2\leq \ldots$ denote the positive ordinates of the non-trivial zeros of the Riemann zeta-function. A result first announced by Selberg states that there exist absolute constants $\Theta, \vartheta>0$ such that for each $r\in \mathbb{N}$, \[ \limsup_{n\to \infty}\frac{\gamma_{n+r}-\gamma_n}{2\pi r/\log \gamma_n}\geq 1+\frac{\Theta}{

Risham Sidhu, Julia Hockenmaier

We introduce GSU, a text-only grid dataset to evaluate the spatial reasoning capabilities of LLMs over 3 core tasks: navigation, object localization, and structure composition. By forgoing visual inputs, isolating spatial reasoning from perception, we show that while most models grasp basic grid concepts, they struggle with frames of reference relative to an

Shoichi Kamada

In this paper, we show combinatorial identities that represent powers of positive integers using multinomial coefficients, which do not come from the multinomial theorem and the multinomial Vandermonde's convolution.

Luís Martins, Carlos Cardeira, Paulo Oliveira

This letter presents a novel control solution to the robust global position and heading tracking problem for underactuated vehicles, equipped with single-axis thrust and full torque actuation, operating under strict, user-defined actuation limits. The architecture features a saturated position tracking controller augmented with two first-order filters. This

Enzo Fenoglio, Philip Treleaven

Federated computing (FC) enables collaborative computation such as machine learning, analytics, or data processing across distributed organizations keeping raw data local. Built on four architectural pillars, distributed data assets, federated services, standardized APIs, and decentralized services, FC supports sovereignty-preserving collaboration. However,

Hadil Ben Amor, Niruthiha Selvanayagam, Manel Abdellatif, Taher A. Ghaleb

Machine Learning (ML) cloud services, offered by leading providers such as Amazon, Google, and Microsoft, enable the integration of ML components into software systems without building models from scratch. However, the rapid adoption of ML services, coupled with the growing complexity of business requirements, has led to widespread misuses, compromising the

Ahmed Farag Ali

The universal phase $\rev{\ii}^{D+2}$ of the Euclidean de Sitter path integral obstructs a straightforward state-counting interpretation of the Gibbons--Hawking entropy. Building on Maldacena's proposal that specific black-hole observers can reorganize this phase, we derive a general constraint on when such ``real observers'' can succeed. By distinguishing \

Kunlong Liu, Yidian Tian, Pengcheng Li

To time-resolve attosecond electronic dynamics in general photoionization processes, the technique that retrieves the phase of emitted electronic wave packets without intercepting the interactions is essential. Here, we theoretically demonstrate a scheme that uses isolated attosecond pulses (IAPs) to achieve this goal. Our approach utilizes the coherent inte

Weiming Wu, Zi-Jian Cheng, Jie Meng, Peng Zhen

The efficient adjudication of responsibility disputes is pivotal for maintaining marketplace fairness. However, the exponential surge in ride-hailing volume renders manual review intractable, while conventional automated methods lack the reasoning transparency required for quasi-judicial decisions. Although Multimodal LLMs offer a promising paradigm, they fu

Sreelakshmi N, Saparya Suresh, Sudheesh K. Kattumannil

The Sen index and Sen-Shorrocks-Thon (SST) index are widely used measures of poverty indices. Developing reliable inference for these measures enables us to compare these measures in different populations of interest in an effective way. It is important to construct confidence intervals for the Sen index and SST index, which provide better coverage probabili

Peisen Zhao, Xiaopeng Zhang, Mingxing Xu, Ruoyu Sun

While Multimodal Large Language Models (MLLMs) have experienced rapid advancements, their visual encoders frequently remain a performance bottleneck. Conventional CLIP-based encoders struggle with dense spatial tasks due to the loss of visual details caused by low-resolution pretraining and the reliance on noisy, coarse web-crawled image-text pairs. To overc

Thuy Truong Tran, Minh Kha Do, Phuc Nguyen Duy, Min Hun Lee

Medical anomaly detection (MAD) and segmentation play a critical role in assisting clinical diagnosis by identifying abnormal regions in medical images and localizing pathological regions. Recent CLIP-based studies are promising for anomaly detection in zero-/few-shot settings, and typically rely on global representations and weak supervision, often producin

Ang Li, Xinyang Gong, Bozhou Chen, Yunlong Lu

We present ShuttleEnv, an interactive and data-driven simulation environment for badminton, designed to support reinforcement learning and strategic behavior analysis in fast-paced adversarial sports. The environment is grounded in elite-player match data and employs explicit probabilistic models to simulate rally-level dynamics, enabling realistic and inter

Alvin Zhu, Mingzhang Zhu, Beom Jun Kim, Jose Victor S. H. Ramos

Scaling dexterous robot learning is constrained by the difficulty of collecting high-quality demonstrations across diverse operators. Existing wearable interfaces often trade comfort and cross-user adaptability for kinematic fidelity, while embodiment mismatch between demonstration and deployment requires visual post-processing before policy training. We pre

Wuqi Wang, Haochen Yang, Baolu Li, Jiaqi Sun

The low-light conditions are challenging to the vision-centric perception systems for autonomous driving in the dark environment. In this paper, we propose a new benchmark dataset (named DarkDriving) to investigate the low-light enhancement for autonomous driving. The existing real-world low-light enhancement benchmark datasets can be collected by controllin

Abhishek Balakrishna, Animikh Biswas

We revisit and sharpen a recent observable regularity criterion for the three-dimensional Navier-Stokes equations on the periodic cube by requiring only finitely many measurements of the flow on a given time interval. Two data models are treated: (i) modal observations (a finite set of low Fourier modes), and (ii) nodal observations, i.e. values of the veloc

Do Thi Hoai Pham Tuyet Nhung, Pierre Darriulat

The present article is written in the wake of a recently published study of the relation between the light curves of Long Period Variables and their evolution along the Asymptotic Giant Branch (AGB). It introduces a pair of new parameters that describe the shape of the ascending branches of such light curves. This parameterization reveals strong correlations

Luke Langford, Ahmad K. Omar

The phase separation of purely-repulsive particles induced by self-propulsion is among the most well-studied non-equilibrium phase transitions. However, some notable features of this transition remain open questions, including the origin of bubbles within the dense phase in two dimensions. Various explanations have been proposed, ranging from a reversal of t

Aniruddha Bora, Julie Chalfant, Chryssostomos Chryssostomidis

International shipping produces approximately 3% of global greenhouse gas emissions, yet voyage routing remains dominated by heuristic methods. We present PIER (Physics-Informed, Energy-efficient, Risk-aware routing), an offline reinforcement learning framework that learns fuel-efficient, safety-aware routing policies from physics-calibrated environments gro

M Murshidul Bari, Akif Islam, Mohd Ruhul Ameen, Abu Saleh Musa Miah

The growing use of artificial intelligence (AI) in education, professional work, and everyday problem-solving has raised important questions about its effect on human reasoning. While AI can improve efficiency, save time, and support learning, repeated dependence on it may also encourage cognitive offloading, reduce productive struggle, and weaken independen

Angshul Majumdar

We study the exact decision problem for feedback capacity of finite-state channels (FSCs). Given an encoding $e$ of a binary-input binary-output rational unifilar FSC with specified rational initial distribution, and a rational threshold $q$, we ask whether the feedback capacity satisfies $C_{fb}(W_e, \pi_{1,e}) \ge q$. We prove that this exact threshold pro

Tian Cao, Ping Wang, Tianfeng Wu, Kaile Wang

In dynamically varying optical wireless communication (OWC) links, conventional quadrature amplitude modulation (QAM) in optical orthogonal frequency-division multiplexing (OFDM) requires frequent channel estimation and equalization, incurring pilot overhead and processing latency. This paper proposes a virtual polarization modulation (VPM)-based direct-curr

Chunxu Zhang, Zhiheng Xue, Guodong Long, Weipeng Zhang

User-centric recommendation has become essential for delivering personalized services, as it enables systems to adapt to users' evolving behaviors while respecting their long-term preferences and privacy constraints. Although federated learning offers a promising alternative to centralized training, existing approaches largely overlook user behavior dynamics

Hongbing Li, Jiamin Liu, Shuo Zhang, Bo Xiao

Grounded Multimodal Named Entity Recognition (GMNER) identifies named entities, including their spans and types, in natural language text and grounds them to the corresponding regions in associated images. Most existing approaches split this task into two steps: they first detect objects using a pre-trained general-purpose detector and then match named entit

Jesse Gell-Redman, Emanuel József Godfried, Justin Tzou, Leo Tzou

This paper deals with the mean first escape time of Brownian motion on asymptotically hyperbolic and gas giant surfaces. We show that for a boundary defining function $\rho$, the mean first escape time $u_\epsilon(x)$ from the truncated Riemannian surface with an asymptotically hyperbolic metric $(M_\epsilon,\bar{g}/\rho^2) = (\{x\in M:\rho(x)\geq \epsilon\}

Yuelin Zhang, Sijie Cheng, Chen Li, Zongzhao Li

Accurately estimating task progress is critical for embodied agents to plan and execute long-horizon, multi-step tasks. Despite promising advances, existing Vision-Language Models (VLMs) based methods primarily leverage their video understanding capabilities, while neglecting their complex reasoning potential. Furthermore, processing long video trajectories

Huan Song, Shuyu Tian, Qingfei Zhao, Wenhao Hong

We present Ruyi2.5, a multimodal familial model built on the AI Flow framework. Extending Ruyi2's "Train Once, Deploy Many" paradigm to the multimodal domain, Ruyi2.5 constructs a shared-backbone architecture that co-trains models of varying scales within a single unified pipeline, ensuring semantic consistency across all deployment tiers. Built upon Ruyi2.5

Panuganti Chirag Sai, Gandholi Sarat, R. Raghunatha Sarma, Venkata Kalyan Tavva

Reducing latency and energy consumption is critical to improving the efficiency of memory systems in modern computing. This work introduces ReLMXEL (Reinforcement Learning for Memory Controller with Explainable Energy and Latency Optimization), a explainable multi-agent online reinforcement learning framework that dynamically optimizes memory controller para

Timothy Oh

Backpropagation has enabled modern deep learning but is difficult to realize as an online, fully distributed hardware learning system due to global error propagation, phase separation, and heavy reliance on centralized memory. Predictive coding offers an alternative in which inference and learning arise from local prediction-error dynamics between adjacent l

Wenhui Du, Kaiying Dou, Ying Dai, Zeyan Wang

Controlling the skyrmion Hall effect (SkHE) is pivotal for developing topological spintronics but typically relies on magnetic field reversal. Here, we demonstrate a general strategy for the purely electrical switching of the SkHE in two-dimensional altermagnets. Through symmetry and model analysis, we reveal that the intrinsic altermagnetic symmetry imposes

Haiyang Yan, Hongyun Zhou, Peng Xu, Xiaoxue Feng

Despite rapid developments and widespread applications of MLLM agents, they still struggle with long-form video understanding (LVU) tasks, which are characterized by high information density and extended temporal spans. Recent research on LVU agents demonstrates that simple task decomposition and collaboration mechanisms are insufficient for long-chain reaso

Md Sifat, Sania Akter, Akif Islam, Md. Ekramul Hamid

Deep learning has become an important tool for Alzheimer&#39;s disease (AD) classification from structural MRI. Many existing studies analyze individual 2D slices extracted from MRI volumes, while clinical neuroimaging practice typically relies on the full three dimensional structure of the brain. From this perspective, volumetric analysis may better capture

Bangju Han, Yingqi Wang, Huang Qing, Tiyuan Li

Culture-expressions, such as idioms, slang, and culture-specific items (CSIs), are pervasive in natural language and encode meanings that go beyond literal linguistic form. Accurately translating such expressions remains challenging for machine translation systems. Despite this, existing benchmarks remain fragmented and do not provide a systematic framework

Hongze Liu, Chang Guo, Yingzeng Li, Mengru Wang

The transition to open, distributed Multi-Agent Systems (MAS) promises scalable intelligence but introduces a non-trivial tension: maximizing global efficiency requires cooperative, resource-aware scheduling, yet autonomous agents may be self-interested and cannot be managed by a centralized controller. Prior approaches fall short in two key areas: they typi

Zahin Sufiyan, Shadan Golestan, Yoshihiro Mitsuka, Shotaro Miwa

Generative Flow Networks for continuous scenarios (CFlowNets) have shown promise in solving sequential decision-making tasks by learning stochastic policies using a flow and a retrieval network. Despite their demonstrated efficiency compared to state-of-the-art Reinforcement Learning (RL) algorithms, their practical application in robotic control tasks is co

Zhenyang Chen, Alan Tian, Liquan Wang, Benjamin Joffe

Despite strong multi-task pretraining, existing policies often exhibit poor task steerability. For example, a robot may fail to respond to a new instruction ``put the bowl in the sink" when moving towards the oven, executing ``close the oven", even though it can complete both tasks when executed separately. We propose ReSteer, a framework to quantify and imp

Yu Tao, Jie Tang, Xuan Wei

Changing-look active galactic nuclei (CL AGNs) provide a valuable opportunity to study optical variability associated with changes in accretion state. We investigate the optical variability of turn-on CL AGNs, selected through the emergence or strengthening of broad emission lines, using g-band light curves from the Zwicky Transient Facility. For a final sam

Yongkang Sun, Zhihao Ding, Huiqiang Wang, Reynold Cheng

In data lakes, information on the same subject is often fragmented across multiple tables. Table union search aims to find the top-k tables that can be unioned with a query table to extend it with more rows, without relying on metadata or ground-truth labels. Existing methods are mainly column-centric: they focus on modeling column unionability scores using

Hua Chen, Linke Yu, Tuo Wu, Maged Elkashlan

Current reconfigurable intelligent surface (RIS)-aided near-field (NF) localization methods assume the RIS position is known a priori, and it has limited their practical applicability. This paper applies a hybrid RIS (HRIS) at an unknown position to locate non-line-of-sight (NLOS) NF targets. To this end, we first propose a two-stage gridless localization fr

Weihua Xiao, Jason Blocklove, Matthew DeLorenzo, Johann Knechtel

GenAI Units In Digital Design Education (GUIDE) is an open courseware repository with runnable Google Colab labs and other materials. We describe the repository's architecture and educational approach based on standardized teaching units comprising slides, short videos, runnable labs, and related papers. This organization enables consistency for both the stu

Ramiro Carrillo-Catalán

This article develops an algebraic model of the 260-day Central Mexican ritual calendar, the \textit{Tonalpohualli}. We represent the calendar as the cyclic group $\mathbb{Z}_{13}\oplus\mathbb{Z}_{20}$, where each day name is encoded by a numeral-sign pair. From this model, we derive explicit correspondences between day numbers and day names through group ac

Jianzhang Zhang, Yijing Tian, Jiwang Qu, Chuang Liu

Story visualization requires generating sequential imagery that aligns semantically with evolving narratives while maintaining rigorous consistency in character identity and visual style. However, existing methodologies often struggle with subject inconsistency and identity drift, particularly when depicting complex interactions or extended narrative arcs. T

Rongke Lyu, Marina Vannucci, Suprateek Kundu

As a general and robust alternative to traditional mean regression models, quantile regression avoids the assumption of normally distributed errors, making it a versatile choice when modeling outcomes such as cognitive scores that typically have skewed distributions. Motivated by an application to Alzheimer's disease data where the aim is to explore how brai

Martin Jouve-Genty, Han Su, Sota Sato, Jie An

Modern cyber-physical systems are complex, and requirements are often written in Signal Temporal Logic (STL). Writing the right STL is difficult in practice; engineers benefit from concrete executions that illustrate what a specification actually admits. Trace synthesis addresses this need, but a single witness rarely suffices to understand intent or explore

Jin Xie, Songze Li, Guang Cheng

Retrieval-Augmented Generation (RAG) systems introduce a critical vulnerability: contextual leakage, where adversaries exploit instruction-following to exfiltrate Personally Identifiable Information (PII) via adaptive extraction. Current defenses force a rigid trade-off between semantic utility and latency. We present SEAL-Tag, a privacy-preserving runtime e

Daniel Bartl, Shahar Mendelson

Let $G, G_1,\dots,G_N$ be independent copies of a standard gaussian random vector in $\mathbb{R}^d$ and denote by $\Gamma = \sum_{i=1}^N \langle G_i,\cdot\rangle e_i$ the standard gaussian ensemble. We show that, for any set $A\subset S^{d-1}$, with exponentially high probability, \[ \sup_{x\in A} \frac{1}{N}\sum_{i=1}^N \big| (\Gamma x)^\sharp_i - q_i\big|

Henrique Bursztyn

These notes are based on an introductory minicourse on Poisson geometry given at CRM, Barcelona, in July 2022. They mostly contain foundational material, including motivating questions and key examples of Poisson structures, and highlight some recent tools and developments. The text is largely structured through exercises that lead readers to the desired con

Timothy Duff, Kisun Lee

We develop a certified numerical algorithm for computing Galois/monodromy groups of parametrized polynomial systems. Our approach employs certified homotopy path tracking to guarantee the correctness of the monodromy action produced by the algorithm, and builds on previous ``homotopy graph" frameworks. We conduct extensive experiments with an implementation

Jessica Striker, Bridget Eileen Tenner

In a recent preprint, Mike Cummings showed that the smooth components of suitably parametrized Springer fibers are in bijection with contracted, fully reduced Pl\"ucker degree-two $\mathfrak{sl}_r$-webs of standard type and that are forests. He showed these are enumerated by sequence A116731 in the OEIS, which is equinumerous with permutations avoiding the p

Aaron Blas Pereda, Diego Sulca

Let $\mathfrak{o}$ be a compact discrete valuation ring and $n\geq 2$. We introduce a method to study the cotype zeta function of subalgebras of $\mathfrak{o}^n$. This multivariable series encodes the number of finite-index subalgebras $\Lambda$ of the $\mathfrak{o}$-algebra $\mathfrak{o}^n$ of a given elementary divisor type. We express this zeta function a

Haichou Li, Tao Qian

We introduce Hilbertian Hardy--Sobolev spaces on tube domains over convex cones and develop their structural theory from a Fourier-analytic point of view. We first establish a Paley--Wiener type representation, which identifies these spaces with weighted $L^2$ spaces on the dual cone and reveals their intrinsic Fourier structure. This representation leads na

Steve Butler, Vera Choi, Joel Jeffries, Nina McCambridge

Juggling patterns can be mathematically modeled as closed walks within directed state graphs. In this paper, we present a unified framework of unbounded juggling patterns and its variations (including multiplex, colored, and passing) primarily through the formalism of the juggling state. By extending this state-based approach and utilizing combinatorial tool

Chunxi Chen, Jingwen Zhang, Chao Yu, Fan Liu

It has been shown that the channel state information (CSI) of a Wi-Fi system can be exploited to localize Wi-Fi devices or track trajectory of a moving target. In the existing literature, both sensing tasks are treated separately and some prior information is usually requested, including the signal fingerprints, the locations of some anchor devices in the Wi

Linhao Li, Zhen Bi, Weiguang Cao

Decoherence in realistic quantum platforms motivates a mixed-state notion of topological phases of matter, including average symmetry-protected topological (ASPT) phases. Alongside this progress, generalized symmetries--notably noninvertible and dipole symmetries--have become powerful organizing principles for exotic quantum phases, yet their implications fo

Sayed Chhattan Shah, Andrea Townsend-Nicholson, Spiros Denaxas, Pablo Lamata

Colorectal cancer, inflammatory bowel disease, and diverticular disease are progressive conditions that affect millions of individuals worldwide and impose substantial clinical and economic burdens. Early detection and personalized management are essential for slowing disease progression and improving patient outcomes. Current care pathways rely primarily on

Liyuan Chen, Shilong Li, Jiangpeng Yan, Shuoling Liu

Large Language Models (LLMs) are rapidly transitioning from static Natural Language Processing (NLP) tasks including sentiment analysis and event extraction to acting as dynamic decision-making agents in complex financial environments. However, the evolution of LLMs into autonomous financial agents faces a significant dilemma in evaluation paradigms. Direct

Guangli Li, Canbiao Wu, Na Tian, Li Zhang

Electroencephalography (EEG)-based emotion recognition suffers from severe performance degradation when models are transferred across heterogeneous datasets due to physiological variability, experimental paradigm differences, and device inconsistencies. Existing domain adversarial methods primarily enforce global marginal alignment and often overlook class-c

Yi Ting Shen, Kentaroh Toyoda, Alex Leung

The Model Context Protocol (MCP) introduces a structurally distinct attack surface that existing threat frameworks, designed for traditional software systems or generic LLM deployments, do not adequately cover. This paper presents MCP-38, a protocol-specific threat taxonomy consisting of 38 threat categories (MCP-01 through MCP-38). The taxonomy was derived

Ariel Linden

Background: Interrupted time series analysis (ITSA) is widely used to evaluate health policy and intervention effects. While multiple-group ITSA (MG-ITSA) improves causal inference by incorporating a control group, residual confounding from unmeasured time-varying factors may remain. The triple-difference interrupted time series (DDD-ITSA) design extends thi

Huamin Chen, Xunzhuo Liu, Yuhan Liu, Junchen Jiang

How many tokens can a GPU inference cluster deliver per watt? Across deployments of identical hardware, the answer varies by 40x -- not because of software inefficiency, but because of the serving context window. We derive the 1/W law: tokens per watt halves every time the context window doubles. A larger context window shrinks the KV-cache concurrency limit

Joyal John Abraham, Yaqian Guo, Yuliia Shemerliuk, Sebastian Selter

The use of antiferromagnets in magnetoelectronic devices as counterparts of ferromagnets is a new, rapidly developing trend in spintronics that leverages antiferromagnetic (AFM) magnons for transmitting of spin currents. Van der Waals (vdW) antiferromagnets are particularly attractive in this respect as they possess tunable magnetic properties and can be eas

Noam H. Rotenberg, Andreia V. Faria, Brian Caffo

Ordinal data is widely prevalent in clinical and other domains, yet there is a lack of both modern, machine-learning based methods and publicly available software to address it. In this paper, we present a model-agnostic method of ordinal classification, which can apply any non-ordinal classification method in an ordinal fashion. We also provide an open-sour

Naichuan Zheng, Hailun Xia, Zepeng Sun, Weiyi Li

Skeleton-based action recognition is crucial for multimedia applications but heavily relies on power-hungry Artificial Neural Networks (ANNs), limiting their deployment on resource-constrained edge devices. Spiking Neural Networks (SNNs) provide an energy-efficient alternative; however, existing spiking models for skeleton data often compromise the intrinsic

Henrique Bursztyn, Hudson Lima

We compute the Poisson cohomology of the linear Poisson structure dual to the n-dimensional "book" Lie algebra, defined by [e_0,e_i]=e_i, [e_i,e_j]=0, for i,j=1,...,n-1.

Ayoub Farkane, David Lassounon

Bacterial cancer therapy exploits anaerobic bacteria's ability to target hypoxia tumor regions, yet the interactions among tumor growth, bacterial colonization, oxygen levels, immunosuppressive cytokines, and bacterial communication remain poorly quantified. We present a mathematical model of five coupled nonlinear reaction-diffusion equations in a two-dimen

Lei Wang, Min Huang, Eduard Dragut

Thematic analysis is difficult to scale: manual workflows are labor-intensive, while fully automated pipelines often lack controllability and transparent evaluation. We present \textbf{CentaurTA Studio}, a web-based system for self-improving human--agent collaboration in open coding and theme construction. The system integrates (1) a two-stage human feedback

Nahyun Lee, Guijin Son, Hyunwoo Ko, Chanyoung Kim

We introduce KMMMU, a native Korean benchmark for evaluating multimodal understanding in Korean cultural and institutional settings. KMMMU contains 3,466 questions from exams natively written in Korean, covering nine disciplines and nine visual modality categories, along with a 300-item Korean-specific subset and a hard subset of 627 questions. Unlike transl

Mohamed Mejri, Ashiqur Rasul, Abhijit Chatterjee

Modern convolutional neural networks (CNNs) are workhorses for video and image processing, but fail to adapt to the computational complexity of input samples in a dynamic manner to minimize energy consumption. In this research, we propose DANCE, a fine-grained, input-aware, dynamic pruning framework for 3D CNNs to maximize power efficiency with negligible to

Jussi Keppo, Yuze Li, Gerry Tsoukalas, Nuo Yuan

Recent work shows that pricing with symmetric LLM agents leads to algorithmic collusion. We show that collusion is fragile under the heterogeneity typical of real deployments. In a stylized repeated-pricing model, heterogeneity in patience or data access reduces the set of collusive equilibria. Experiments with open-source LLM agents (totaling over 2,000 com

Abhinava Chatterjee, Chao-Xing Liu

The surface phonon Hall viscosity (PHV)-an acoustic analog of axion electrodynamics-emerges from the strain response of magnetic topological insulators and gives rise to novel acoustic phenomena. In this work, we propose a previously unexplored effect: a phonon polarization-filter mechanism induced by the surface PHV, which generates an interface phonon mode