Tootfinder

Establishing assembly-oriented modular product architectures through Design for Assembly enhanced Modular Function Deployment
Fabio Marco Monetti, Adam Lundstr\"om, Colin de Kwant, Magnus Gyllenskepp, Antonio Maffei
https://arxiv.org/abs/2510.11089

Establishing assembly-oriented modular product architectures through Design for Assembly enhanced Modular Function Deployment
Modular product design has become a strategic enabler for companies seeking to balance product variety, operational efficiency, and market responsiveness, making the alignment between modular architecture and manufacturing considerations increasingly critical. Modular Function Deployment (MFD) is a widely adopted method for defining modular product architectures, yet it lacks systematic support for assembly considerations during early concept and system-level development. This limitation increa…

SLEAN: Simple Lightweight Ensemble Analysis Network for Multi-Provider LLM Coordination: Design, Implementation, and Vibe Coding Bug Investigation Case Study
Matheus J. T. Vargas
https://arxiv.org/abs/2510.10010

SLEAN: Simple Lightweight Ensemble Analysis Network for Multi-Provider LLM Coordination: Design, Implementation, and Vibe Coding Bug Investigation Case Study
We present SLEAN (Simple Lightweight Ensemble Analysis Network), a deterministic framework for coordinating multiple LLM providers through text-based prompt orchestration. Unlike complex multi-agent systems requiring specialized infrastructure, SLEAN operates as a simple prompt bridge between LLMs using .txt templates, requiring no deep technical knowledge for deployment. The three-phase protocol formed by independent analysis, cross-critique, and arbitration, filters harmful AI-generated code …

Structured Sparsity and Weight-adaptive Pruning for Memory and Compute efficient Whisper models
Prasenjit K Mudi, Anshi Sachan, Dahlia Devapriya, Sheetal Kalyani
https://arxiv.org/abs/2510.12666

Structured Sparsity and Weight-adaptive Pruning for Memory and Compute efficient Whisper models
Whisper models have achieved remarkable progress in speech recognition; yet their large size remains a bottleneck for deployment on resource-constrained edge devices. This paper proposes a framework to design fine-tuned variants of Whisper which address the above problem. Structured sparsity is enforced via the Sparse Group LASSO penalty as a loss regularizer, to reduce the number of FLOating Point operations (FLOPs). Further, a weight statistics aware pruning algorithm is proposed. We also des…

Bhasha-Rupantarika: Algorithm-Hardware Co-design approach for Multilingual Neural Machine Translation
Mukul Lokhande, Tanushree Dewangan, Mohd Sharik Mansoori, Tejas Chaudhari, Akarsh J., Damayanti Lokhande, Adam Teman, Santosh Kumar Vishvakarma
https://arxiv.org/abs/2510.10676

Bhasha-Rupantarika: Algorithm-Hardware Co-design approach for Multilingual Neural Machine Translation
This paper introduces Bhasha-Rupantarika, a light and efficient multilingual translation system tailored through algorithm-hardware codesign for resource-limited settings. The method investigates model deployment at sub-octet precision levels (FP8, INT8, INT4, and FP4), with experimental results indicating a 4.1x reduction in model size (FP4) and a 4.2x speedup in inference speed, which correlates with an increased throughput of 66 tokens/s (improvement by 4.8x). This underscores the importance…

Wireless Datasets for Aerial Networks
Amir Hossein Fahim Raouf, Donggu Lee, Mushfiqur Rahman, Saad Masrur, Gautham Reddy, Cole Dickerson, Md Sharif Hossen, Sergio Vargas Villar, An{\i}l G\"urses, Simran Singh, Sung Joon Maeng, Martins Ezuma, Christopher Roberts, Mohamed Rabeek Sarbudeen, Thomas J. Zajkowski, Magreth Mushi, Ozgur Ozdemir, Ram Asokan, Ismail Guvenc, Mihail L. Sichitiu, Rudra Dutta

Wireless Datasets for Aerial Networks
The integration of unmanned aerial vehicles (UAVs) into 5G-Advanced and future 6G networks presents a transformative opportunity for wireless connectivity, enabling agile deployment and improved LoS communications. However, the effective design and optimization of these aerial networks depend critically on high-quality, empirical data. This paper provides a comprehensive survey of publicly available wireless datasets collected from an airborne platform called Aerial Experimentation and Research…

Coordinated Beamforming for Networked Integrated Communication and Multi-TMT Localization
Meidong Xia, Zhenyao He, Wei Xu, Yongming Huang, Derrick Wing Kwan Ng, Naofal Al-Dhahir
https://arxiv.org/abs/2510.04600

Coordinated Beamforming for Networked Integrated Communication and Multi-TMT Localization
Networked integrated sensing and communication (ISAC) has gained significant attention as a promising technology for enabling next-generation wireless systems. To further enhance networked ISAC, delegating the reception of sensing signals to dedicated target monitoring terminals (TMTs) instead of base stations (BSs) offers significant advantages in terms of sensing capability and deployment flexibility. Despite its potential, the coordinated beamforming design for networked integrated communica…

Beyond Revenue and Welfare: Counterfactual Analysis of Spectrum Auctions with Application to Canada's 3800MHz Allocation
Sara Jalili Shani, Kris Joseph, Michael B. McNally, James R. Wright
https://arxiv.org/abs/2512.08106 https://arxiv.org/pdf/2512.08106 https://arxiv.org/html/2512.08106
arXiv:2512.08106v1 Announce Type: new
Abstract: Spectrum auctions are the primary mechanism through which governments allocate scarce radio frequencies, with outcomes that shape competition, coverage, and innovation in telecommunications markets. While traditional models of spectrum auctions often rely on strong equilibrium assumptions, we take a more parsimonious approach by modeling bidders as myopic and straightforward: in each round, firms simply demand the bundle that maximizes their utility given current prices. Despite its simplicity, this model proves effective in predicting the outcomes of Canada's 2023 auction of 3800 MHz spectrum licenses. Using detailed round-by-round bidding data, we estimate bidders' valuations through a linear programming framework and validate that our model reproduces key features of the observed allocation and price evolution. We then use these estimated valuations to simulate a counterfactual auction under an alternative mechanism that incentivizes deployment in rural and remote regions, aligning with one of the key objectives set out in the Canadian Telecommunications Act. The results show that the proposed mechanism substantially improves population coverage in underserved areas. These findings demonstrate that a behavioral model with minimal assumptions is sufficient to generate reliable counterfactual predictions, making it a practical tool for policymakers to evaluate how alternative auction designs may influence future outcomes. In particular, our study demonstrates a method for counterfactual mechanism design, providing a framework to evaluate how alternative auction rules could advance policy goals such as equitable deployment across Canada.
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Stratum: System-Hardware Co-Design with Tiered Monolithic 3D-Stackable DRAM for Efficient MoE Serving
Yue Pan, Zihan Xia, Po-Kai Hsu, Lanxiang Hu, Hyungyo Kim, Janak Sharda, Minxuan Zhou, Nam Sung Kim, Shimeng Yu, Tajana Rosing, Mingu Kang
https://arxiv.org/abs/2510.05245

Stratum: System-Hardware Co-Design with Tiered Monolithic 3D-Stackable DRAM for Efficient MoE Serving
As Large Language Models (LLMs) continue to evolve, Mixture of Experts (MoE) architecture has emerged as a prevailing design for achieving state-of-the-art performance across a wide range of tasks. MoE models use sparse gating to activate only a handful of expert sub-networks per input, achieving billion-parameter capacity with inference costs akin to much smaller models. However, such models often pose challenges for hardware deployment due to the massive data volume introduced by the MoE laye…

YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection
Ranjan Sapkota, Rahul Harsha Cheppally, Ajay Sharda, Manoj Karkee
https://arxiv.org/abs/2509.25164

YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection
This study presents Key Architectural Enhancements and Performance Benchmarking of Ultralytics YOLO26 for real-time edge object detection, providing a comprehensive overview of the design principles of YOLO26, technological advances, and deployment readiness. YOLO26, released in September 2025 by Ultralytics, represents the newest and most cutting-edge member of the You Only Look Once (YOLO) family, engineered to push the boundaries of efficiency and accuracy on edge and low-power devices. This…

NEURODNAAI: Neural pipeline approaches for the advancing dna-based information storage as a sustainable digital medium using deep learning framework
Rakesh Thakur, Lavanya Singh, Yashika, Manomay Bundawala, Aruna Kumar
https://arxiv.org/abs/2510.02417

NEURODNAAI: Neural pipeline approaches for the advancing dna-based information storage as a sustainable digital medium using deep learning framework
DNA is a promising medium for digital information storage for its exceptional density and durability. While prior studies advanced coding theory, workflow design, and simulation tools, challenges such as synthesis costs, sequencing errors, and biological constraints (GC-content imbalance, homopolymers) limit practical deployment. To address this, our framework draws from quantum parallelism concepts to enhance encoding diversity and resilience, integrating biologically informed constraints with…

Single and Multi-Objective Optimization of Distributed Acoustic Sensing Cable Layouts for Geophysical Applications
Dominik Strutz, Tjeerd Kiers, Andrew Curtis
https://arxiv.org/abs/2510.07531

Single and Multi-Objective Optimization of Distributed Acoustic Sensing Cable Layouts for Geophysical Applications
We present a systematic approach to optimise distributed acoustic sensing (DAS) fibre-optic cable layouts using global optimisation techniques. Our method represents cable geometries using splines, enabling efficient exploration of layouts while respecting physical deployment constraints. The use of evolutionary algorithms enables single and multi-objective optimisation, taking into account complex design constraints such as terrain, accesibility, exclusion zones and cable length, while allowin…

A borehole muon detector with SiPM-on-tile technology
Miguel Arratia, Jiajun Huang, Sean Preins, Sebastian Ritter, Christian P. Romero, Sebastian Tapia
https://arxiv.org/abs/2510.04036

A borehole muon detector with SiPM-on-tile technology
We developed a compact and rugged muon detector designed for deployment in boreholes. The detector uses a SiPM-on-tile approach in which silicon photomultipliers are directly coupled to scintillator tiles, thereby eliminating the need for wavelength-shifting fibers and long scintillator bars. The modular design is based on a 64-channel unit, 140~cm in length and 80~mm in diameter, composed of $5 \times 5$~cm$^{2}$ scintillator tiles coupled to SiPMs, powered and read out using off-the-shelf ele…

Key Considerations for Auto-Scaling: Lessons from Benchmark Microservices
Majid Dashtbani, Ladan Tahvildari
https://arxiv.org/abs/2510.02585 https://arxiv.…

Key Considerations for Auto-Scaling: Lessons from Benchmark Microservices
Microservices have become the dominant architectural paradigm for building scalable and modular cloud-native systems. However, achieving effective auto-scaling in such systems remains a non-trivial challenge, as it depends not only on advanced scaling techniques but also on sound design, implementation, and deployment practices. Yet, these foundational aspects are often overlooked in existing benchmarks, making it difficult to evaluate autoscaling methods under realistic conditions. In this pap…

Design and deployment of a fast neural network for measuring the properties of muons originating from displaced vertices in the CMS Endcap Muon Track Finder
Efe Yigitbasi (on behalf of CMS Collaboration)
https://arxiv.org/abs/2509.21062

Design and deployment of a fast neural network for measuring the properties of muons originating from displaced vertices in the CMS Endcap Muon Track Finder
We report on the development, implementation, and performance of a fast neural network used to measure the transverse momentum in the CMS Level-1 Endcap Muon Track Finder. The network aims to improve the triggering efficiency of muons produced in the decays of long-lived particles (LLPs). We implemented it in firmware for a Xilinx Virtex-7 FPGA and deployed it during the LHC Run 3 data-taking in 2023. The new displaced muon triggers that use this algorithm broaden the phase space accessible to …

Agentic AI for Financial Crime Compliance
Henrik Axelsen, Valdemar Licht, Jan Damsgaard
https://arxiv.org/abs/2509.13137 https://arxiv.org/pdf/2509.13137…

Agentic AI for Financial Crime Compliance
The cost and complexity of financial crime compliance (FCC) continue to rise, often without measurable improvements in effectiveness. While AI offers potential, most solutions remain opaque and poorly aligned with regulatory expectations. This paper presents the design and deployment of an agentic AI system for FCC in digitally native financial platforms. Developed through an Action Design Research (ADR) process with a fintech firm and regulatory stakeholders, the system automates onboarding, m…

Substrate-Timing-Independence for Meta-State Stability of Distributed Robotic Swarms
Tinapat Limsila, Mehul Sharma, Paulo Garcia
https://arxiv.org/abs/2509.16492 https://…

Substrate-Timing-Independence for Meta-State Stability of Distributed Robotic Swarms
Emergent properties in distributed systems arise due to timing unpredictability; asynchronous state evolution within each sub-system may lead the macro-system to faulty meta-states. Empirical validation of correctness is often prohibitively expensive, as the size of the state-space is too large to be tractable. In robotic swarms this problem is exacerbated, when compared to software systems, by the variability of the implementation substrate across the design, or even the deployment, process. W…

Introducing Large Language Models in the Design Flow of Time Sensitive Networking
Rubi Debnath, Luxi Zhao, Mohammadreza Barzegaran, Sebastian Steinhorst
https://arxiv.org/abs/2509.26368

Introducing Large Language Models in the Design Flow of Time Sensitive Networking
The growing demand for real-time, safety-critical systems has significantly increased both the adoption and complexity of Time Sensitive Networking (TSN). Configuring an optimized TSN network is highly challenging, requiring careful planning, design, verification, validation, and deployment. Large Language Models (LLMs) have recently demonstrated strong capabilities in solving complex tasks, positioning them as promising candidates for automating end-to-end TSN deployment, referred to as TSN or…

Structuring Automotive Data for Systems Engineering: A Taxonomy-Based Approach
Carl Philipp Hohl, Philipp Reis, Tobias Sch\"urmann, Stefan Otten, Eric Sax
https://arxiv.org/abs/2510.00963

Structuring Automotive Data for Systems Engineering: A Taxonomy-Based Approach
Vehicle data is essential for advancing data-driven development throughout the automotive lifecycle, including requirements engineering, design, verification, and validation, and post-deployment optimization. Developers currently collect data in a decentralized and fragmented manner across simulations, test benches, and real-world driving, resulting in data silos, inconsistent formats, and limited interoperability. This leads to redundant efforts, inefficient integration, and suboptimal use of …

Intelligent Mode Sorting in Turbulence with Task-Dependent Optical Neural Networks
Christopher R. Rawlings, Mitchell A. Cox
https://arxiv.org/abs/2509.20818 https://

Intelligent Mode Sorting in Turbulence with Task-Dependent Optical Neural Networks
The practical deployment of high-capacity free-space optical communication is fundamentally limited by atmospheric turbulence, a challenge that conventional mode-sorting techniques have failed to overcome. While engineered optical computers like diffractive networks offer a potential solution, their design complexity remains a significant barrier. Here, we introduce and experimentally validate a methodology for task-dependent hardware design, demonstrating a simple, continuous-wave-driven multi…

Expert-as-a-Service: Towards Efficient, Scalable, and Robust Large-scale MoE Serving
Ziming Liu, Boyu Tian, Guoteng Wang, Zhen Jiang, Peng Sun, Zhenhua Han, Tian Tang, Xiaohe Hu, Yanmin Jia, Yan Zhang, He Liu, Mingjun Zhang, Yiqi Zhang, Qiaoling Chen, Shenggan Cheng, Mingyu Gao, Yang You, Siyuan Feng
https://arxiv.org/abs/2509.17863…

Expert-as-a-Service: Towards Efficient, Scalable, and Robust Large-scale MoE Serving
Mixture-of-Experts (MoE) models challenge serving infrastructures with dynamic, sparse expert utilization, causing instability on conventional systems designed for dense architectures. We propose EaaS, a novel serving system to enable efficient, scalable, and robust MoE deployment. Our system disaggregates MoE modules into independent, stateless services. This design enables fine-grained resource scaling and provides inherent fault tolerance by decoupling compute units. The architecture is powe…

Kalman Filtering-Assisted Node Deployment for Distributed OTFS-ISAC: A Geometry-Aware Design for the Joint Sensing and Communication
Jyotsna Rani, Kuntal Deka, Ganesh Prasad, Zilong Liu
https://arxiv.org/abs/2509.16700

Kalman Filtering-Assisted Node Deployment for Distributed OTFS-ISAC: A Geometry-Aware Design for the Joint Sensing and Communication
Integrated sensing and communication (ISAC) is a key enabler for next-generation wireless networks, offering spectrum efficiency and reduced hardware complexity. While monostatic ISAC has been well studied, its limited spatial diversity reduces reliability in high-mobility scenarios. Distributed ISAC alleviates this via cooperative nodes, but conventional OFDM-based designs remain vulnerable to Doppler shifts and multipath fading. Orthogonal time frequency space (OTFS) modulation has recently e…

An Internet of Intelligent Things Framework for Decentralized Heterogeneous Platforms
Vadim Allayev, Mahbubur Rahman
https://arxiv.org/abs/2509.10507 https://

An Internet of Intelligent Things Framework for Decentralized Heterogeneous Platforms
Internet of Intelligent Things (IoIT), an emerging field, combines the utility of Internet of Things (IoT) devices with the innovation of embedded AI algorithms. However, it does not come without challenges, and struggles regarding available computing resources, energy supply, and storage limitations. In particular, many impediments to IoIT are linked to the energy-efficient deployment of machine learning (ML)/deep learning (DL) models in embedded devices. Research has been conducted to design …