# Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models

arXiv: https://arxiv.org/abs/2508.00804 · HTML: https://mlsanity.com/paper/2508.00804/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Resurrecting Recurrent Neural Networks for Long Sequences](https://mlsanity.com/paper/2303.06349/) (2023)
- [Online learning of long-range dependencies](https://mlsanity.com/paper/2305.15947/) (2023)
- [Real-Time Recurrent Reinforcement Learning](https://mlsanity.com/paper/2311.04830/) (2023)

## Built on by (most cited first, 1 of 1)

- [Adaptive Control in Autonomous Driving via Real-Time Recurrent RL](https://mlsanity.com/paper/2602.02236/) (2026)

## Abstract (arXiv)

This paper introduces a new approach for fine-tuning the predictions of structured state space models (SSMs) at inference time using real-time recurrent learning. While SSMs are known for their efficiency and long-range modeling capabilities, they are typically trained offline and remain static during deployment. Our method enables online adaptation by continuously updating model parameters in response to incoming data. We evaluate our approach for linear-recurrent-unit SSMs using a small carbon emission dataset collected from embedded automotive hardware. Experimental results show that our method consistently reduces prediction error online during inference, demonstrating its potential for dynamic, resource-constrained environments.