# Multi-Span Optical Power Spectrum Evolution Modeling using ML-based Multi-Decoder Attention Framework

arXiv: https://arxiv.org/abs/2503.17072 · HTML: https://mlsanity.com/paper/2503.17072/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 21 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Improving Generalization Performance by Switching from Adam to SGD](https://mlsanity.com/paper/1712.07628/) (2017)
- [Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoders](https://mlsanity.com/paper/2206.02079/) (2022)
- [Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain](https://mlsanity.com/paper/2308.02233/) (2023)

## Built on by (most cited first, 2 of 2)

- [Agentic AI for Scalable and Robust Optical Systems Control](https://mlsanity.com/paper/2602.20144/) (2026)
- [Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum](https://mlsanity.com/paper/2507.21728/) (2025)

## Abstract (arXiv)

We implement a ML-based attention framework with component-specific decoders, improving optical power spectrum prediction in multi-span networks. By reducing the need for in-depth training on each component, the framework can be scaled to multi-span topologies with minimal data collection, making it suitable for brown-field scenarios.