# Next-generation reservoir computing validated by classification task

arXiv: https://arxiv.org/abs/2512.12903 · HTML: https://mlsanity.com/paper/2512.12903/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing](https://mlsanity.com/paper/2203.13294/) (2022)
- [Infinite-dimensional next-generation reservoir computing](https://mlsanity.com/paper/2412.09800/) (2024)
- [Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalisation](https://mlsanity.com/paper/1610.06268/) (2016)
- [Photonic machine learning implementation for signal recovery in optical communications](https://mlsanity.com/paper/1710.01107/) (2017)

## Abstract (arXiv)

An emerging computing paradigm, so-called next-generation reservoir computing (NG-RC) is investigated. True to its namesake, NG-RC requires no actual reservoirs for input data mixing but rather computing the polynomial terms directly from the time series inputs. However, benchmark tests so far reported have been one-sided, limited to prediction tasks of temporal waveforms such as Lorenz 63 attractor and Mackey-Glass chaotic signal. We will demonstrate for the first time that NG-RC can perform classification task as good as conventional RC. This validates the versatile computational capability of NG-RC in tasks of both prediction and classification.