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Mazur-Tate elements of non-ordinary modular forms with Serre weight larger than two
Rylan Gajek-Leonard, Antonio Lei
https://arxiv.org/abs/2508.11007 https://

Mazur-Tate elements of non-ordinary modular forms with Serre weight larger than two
Fix an odd prime $p$ and let $f$ be a non-ordinary eigen-cuspform of weight $k$ and level coprime to $p$. Assuming $p>k-1$, we compute asymptotic formulas for the Iwasawa invariants of the Mazur-Tate elements attached to $f$ in terms of the corresponding invariants of the signed $p$-adic $L$-functions. By combining this with a version of mod $p$ multiplicity one, we also obtain descriptions of the $λ$-invariants of Mazur-Tate elements attached to certain higher weight modular forms with Serre …

Visual Perception Engine: Fast and Flexible Multi-Head Inference for Robotic Vision Tasks
Jakub {\L}ucki, Jonathan Becktor, Georgios Georgakis, Robert Royce, Shehryar Khattak
https://arxiv.org/abs/2508.11584

Visual Perception Engine: Fast and Flexible Multi-Head Inference for Robotic Vision Tasks
Deploying multiple machine learning models on resource-constrained robotic platforms for different perception tasks often results in redundant computations, large memory footprints, and complex integration challenges. In response, this work presents Visual Perception Engine (VPEngine), a modular framework designed to enable efficient GPU usage for visual multitasking while maintaining extensibility and developer accessibility. Our framework architecture leverages a shared foundation model backb…

GWKokab: An Implementation to Identify the Properties of Multiple Population of Gravitational Wave Sources
Meesum Qazalbash, Muhammad Zeeshan, Richard O'Shaughnessy
https://arxiv.org/abs/2509.13638

GWKokab: An Implementation to Identify the Properties of Multiple Population of Gravitational Wave Sources
The rapidly increasing sensitivity of gravitational wave detectors is enabling the detection of a growing number of compact binary mergers. These events are crucial for understanding the population properties of compact binaries. However, many previous studies rely on computationally expensive inference frameworks, limiting their scalability. In this work, we present GWKokab, a JAX-based framework that enables modular model building with independent rate for each subpopulation such as BBH, BN…

Optimizing Inter-chip Coupler Link Placement for Modular and Chiplet Quantum Systems
Zefan Du, Pedro Chumpitaz Flores, Wenqi Wei, Juntao Chen, Kaixun Hua, Ying Mao
https://arxiv.org/abs/2509.10409

Optimizing Inter-chip Coupler Link Placement for Modular and Chiplet Quantum Systems
Quantum computing offers unparalleled computational capabilities but faces significant challenges, including limited qubit counts, diverse hardware topologies, and dynamic noise and error rates, which hinder scalability and reliability. Distributed quantum computing, particularly chip-to-chip connections, has emerged as a solution by interconnecting multiple processors to collaboratively execute large circuits. While hardware advancements, such as IBM's Quantum Flamingo, focus on improving inte…

NavMoE: Hybrid Model- and Learning-based Traversability Estimation for Local Navigation via Mixture of Experts
Botao He, Amir Hossein Shahidzadeh, Yu Chen, Jiayi Wu, Tianrui Guan, Guofei Chen, Howie Choset, Dinesh Manocha, Glen Chou, Cornelia Fermuller, Yiannis Aloimonos
https://arxiv.org/abs/2509.12747

NavMoE: Hybrid Model- and Learning-based Traversability Estimation for Local Navigation via Mixture of Experts
This paper explores traversability estimation for robot navigation. A key bottleneck in traversability estimation lies in efficiently achieving reliable and robust predictions while accurately encoding both geometric and semantic information across diverse environments. We introduce Navigation via Mixture of Experts (NAVMOE), a hierarchical and modular approach for traversability estimation and local navigation. NAVMOE combines multiple specialized models for specific terrain types, each of whi…

Investigating the Potential of Multi-Stage Score Fusion in Spoofing-Aware Speaker Verification
Oguzhan Kurnaz, Tomi Kinnunen, Cemal Hanilci
https://arxiv.org/abs/2509.12668 http…

Investigating the Potential of Multi-Stage Score Fusion in Spoofing-Aware Speaker Verification
Despite improvements in automatic speaker verification (ASV), vulnerability against spoofing attacks remains a major concern. In this study, we investigate the integration of ASV and countermeasure (CM) subsystems into a modular spoof-aware speaker verification (SASV) framework. Unlike conventional single-stage score-level fusion methods, we explore the potential of a multi-stage approach that utilizes the ASV and CM systems in multiple stages. By leveraging ECAPA-TDNN (ASV) and AASIST (CM) sub…

AttZoom: Attention Zoom for Better Visual Features
Daniel DeAlcala, Aythami Morales, Julian Fierrez, Ruben Tolosana
https://arxiv.org/abs/2508.03625 https://

AttZoom: Attention Zoom for Better Visual Features
We present Attention Zoom, a modular and model-agnostic spatial attention mechanism designed to improve feature extraction in convolutional neural networks (CNNs). Unlike traditional attention approaches that require architecture-specific integration, our method introduces a standalone layer that spatially emphasizes high-importance regions in the input. We evaluated Attention Zoom on multiple CNN backbones using CIFAR-100 and TinyImageNet, showing consistent improvements in Top-1 and Top-5 cla…

Model predictive quantum control: A modular approach for efficient and robust quantum optimal control
Eya Guizani, Julian Berberich
https://arxiv.org/abs/2509.05167 https://

Model predictive quantum control: A modular approach for efficient and robust quantum optimal control
Model predictive control (MPC) is one of the most successful modern control methods. It relies on repeatedly solving a finite-horizon optimal control problem and applying the beginning piece of the optimal input. In this paper, we develop a modular framework for improving efficiency and robustness of quantum optimal control (QOC) via MPC. We first provide a tutorial introduction to basic concepts of MPC from a QOC perspective. We then present multiple MPC schemes, ranging from simple approaches…

Rotatable Antenna Aided Mixed Near-Field and Far-Field Communications in the Upper Mid-Band: Interference Analysis and Joint Optimization
Yunpu Zhang, Changsheng You, Hing Cheung So, Dusit Niyato
https://arxiv.org/abs/2509.04865

Rotatable Antenna Aided Mixed Near-Field and Far-Field Communications in the Upper Mid-Band: Interference Analysis and Joint Optimization
In this paper, we propose to leverage rotatable antennas (RAs) for improving the communication performance in mixed near-field and far-field communication systems by exploiting a new spatial degree-of-freedom (DoF) offered by antenna rotation to mitigate complex near-field interference and mixed-field interference. Specifically, we investigate a modular RA-enabled mixed-field downlink communication system, where a base station (BS) consisting of multiple RA subarrays communicates with multiple …

Multi-module GRPO: Composing Policy Gradients and Prompt Optimization for Language Model Programs
Noah Ziems, Dilara Soylu, Lakshya A Agrawal, Isaac Miller, Liheng Lai, Chen Qian, Kaiqiang Song, Meng Jiang, Dan Klein, Matei Zaharia, Karel D'Oosterlinck, Christopher Potts, Omar Khattab
https://arxiv.org/abs/2508.04660

Multi-module GRPO: Composing Policy Gradients and Prompt Optimization for Language Model Programs
Group Relative Policy Optimization (GRPO) has proven to be an effective tool for post-training language models (LMs). However, AI systems are increasingly expressed as modular programs that mix together multiple LM calls with distinct prompt templates and other tools, and it is not clear how best to leverage GRPO to improve these systems. We begin to address this challenge by defining mmGRPO, a simple multi-module generalization of GRPO that groups LM calls by module across rollouts and handles…