让状态空间模型在运行中实时调整，提升碳排放预测精度。
Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models
- 用实时循环学习，在推理时动态更新模型参数。
- 在车载硬件数据上，持续降低碳排放预测误差。
- 适合边缘设备等资源受限的实时场景使用。
本文提出一种新方法，通过实时循环学习在推理阶段对结构化状态空间模型（SSMs）进行在线微调。尽管SSMs以高效和长程建模能力著称，但通常离线训练后部署时保持静态。本方法使模型能根据输入数据持续更新参数，实现在线适应。我们在嵌入式车载硬件采集的小型碳排放数据集上评估了线性递归单元（linear-recurrent-unit）SSMs。实验结果表明，该方法在推理过程中持续降低预测误差，展现出在动态、资源受限环境中的应用潜力。
原文摘要 · Abstract (English)
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.
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