Twin Networks
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Related Articles from SNS
TANDEM: Bi-Level Data Mixture Optimization with Twin Networks
arXiv:2606.04401v1 Announce Type: new Abstract: The capabilities of large language models (LLMs) significantly depend on training data drawn from various domains. Optimizing domain-specific mixture ratios can be modeled as a bi-level optimization problem, which we simplify into a single-level penalized form and solve with twin networks: a proxy model trained on primary data and a dynamically updated reference model trained with additional data. Our proposed method, Twin Networks for bi-level...
ARIADNE: AI-RAN Informed Link Adaptation in Digital Twin Network Environments
Announce Type: replace Abstract: Artificial Intelligence (AI)-powered Radio Access Network (RAN) networks have attracted significant attention from both industry and academia. Meanwhile, Digital Twins offer a safe playground for experimenting with AI/Machine Learning (ML)-based solutions for advanced AI-RAN research.
Advancing Network Digital Twin Framework for Generating Realistic Datasets
arXiv:2604.12888v2 Announce Type: replace Abstract: The integration of accurate and reproducible wireless network simulations is a key enabler for research on open, virtualized, and intelligent communication systems. Network Digital Twins (NDTs) provide a scalable alternative to costly and time-consuming measurement campaigns, while enabling controlled experimentation and data generation for data-driven network design. In this paper, we present an open and user-friendly NDT framework that...
SA-DTS: Semantic-Aware Digital Twin Synchronization over 6G Networks
Announce Type: new Abstract: Digital Twins (DTs) are emerging as a cornerstone of the 6G vision, enabling real-time cyber-physical mirroring for smart manufacturing, autonomous vehicles, and remote healthcare. However, maintaining high-fidelity synchronization at scale demands an enormous and sustained uplink bandwidth, threatening both the feasibility and the energy efficiency of large deployments. We propose a Semantic-Aware DT Synchronization (SA-DTS) framework that radically redefines...
Toward Trustworthy Digital Twins in AI Agent-based Wireless Network Optimization: Challenges, Solutions, and Opportunities
arXiv:2511.19961v2 Announce Type: replace Abstract: Optimizing modern wireless networks is exceptionally challenging due to their high dynamism and complexity. While the AI agent powered by reinforcement learning (RL) offers a promising solution, its practical application is limited by prohibitive exploration costs and potential risks in the real world. The emerging digital twin (DT) technology provides a safe and controlled virtual environment for agent training, but its effectiveness...
VaN3Twin: the Multi-Technology V2X Digital Twin with Ray-Tracing in the Loop
arXiv:2505.14184v3 Announce Type: replace Abstract: This paper presents VaN3Twin-the first open-source, full-stack Network Digital Twin (NDT) framework for simulating the coexistence of multiple Vehicle-to-Everything (V2X) communication technologies with accurate physical-layer modeling via ray tracing. VaN3Twin extends the ms-van3t simulator by integrating Sionna Ray Tracer (RT) in the loop, enabling high-fidelity representation of wireless propagation, including diverse Line-of-Sight (LoS)...
ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks
arXiv:2605.12768v2 Announce Type: replace-cross Abstract: Open time-series forecasting (TSF) benchmarks cover retail, energy, weather, and traffic, but supply-chain logistics remains underserved. We introduce ISOMORPH, the first public digital twin of a multi-echelon logistics network with interpretable, user-configurable parameters and modular topology, demand, and control rules. The simulator advances a directed routing graph in discrete time: demand is served from inventory or recorded as...
Twin: Tuning Learning Rate and Weight Decay of Deep Homogeneous Classifiers without Validation
Announce Type: replace Abstract: We introduce Tune without Validation (Twin), a simple and effective pipeline for tuning learning rate and weight decay of homogeneous classifiers without validation sets, eliminating the need to hold out data and avoiding the two-step process. Twin leverages the margin-maximization dynamics of homogeneous networks and an empirical scaling law that links training and test losses across hyper-parameter configurations. This mathematical modeling yields a...
NeuDW-CIM: a 65-nm 0.8-pJ/Sop Reconfigurable Neuromorphic Compute-in-Memory Macro with Nonlinear Dendrites and K-Winners
arXiv:2606.08947v1 Announce Type: new Abstract: This work presents NeuDW-CIM, a highly efficient neuromorphic Compute-in-Memory (CIM) macro for Spiking Neural Networks (SNNs) implemented in 65 nm CMOS. The design introduces a custom twin 9T bit-cell for ternary in-puts/weights and a reconfigurable non-linear In-Memory ADC (IMA).
A Survey of Smart Grid Emerging Use Cases and Relevant 5G and 6G Capabilities and Features
new Abstract: The growing complexity of modern energy systems has led to the adoption of Smart Grid (SG) that use advanced communication technologies to facilitate efficient, reliable, secure, and sustainable energy operation and management. Unlike existing surveys that often treat grid and communication domains separately, this work rigorously quantifies service requirements for high-complexity emerging scenarios. It provides a comprehensive overview of SG architecture that integrates...