# Learning Nonlinear Systems In-Context: From Synthetic Data to Real-World Motor Control

arXiv: https://arxiv.org/abs/2602.07173 · HTML: https://mlsanity.com/paper/2602.07173/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Self-evolving LLM agents, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 15 of its 18 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## Placed in

- Synthetic data and self-improvement for LLMs / [Self-evolving LLM agents](https://mlsanity.com/synth/self-evolving-llm-agents/)

## Builds on (most cited first, 15 of 15)

- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://mlsanity.com/paper/2208.01066/) (2022)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [Transformers as Algorithms: Generalization and Stability in In-context Learning](https://mlsanity.com/paper/2301.07067/) (2023)
- [What Algorithms can Transformers Learn? A Study in Length Generalization](https://mlsanity.com/paper/2310.16028/) (2023)
- [The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains](https://mlsanity.com/paper/2402.11004/) (2024)
- [How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations](https://mlsanity.com/paper/2310.10616/) (2023)
- [Pre-Training to Learn in Context](https://mlsanity.com/paper/2305.09137/) (2023)
- [From system models to class models: An in-context learning paradigm](https://mlsanity.com/paper/2308.13380/) (2023)
- [On the adaptation of in-context learners for system identification](https://mlsanity.com/paper/2312.04083/) (2023)
- [Synthetic data generation for system identification: leveraging knowledge transfer from similar systems](https://mlsanity.com/paper/2403.05164/) (2024)
- [Data-driven feedforward control design for nonlinear systems: A control-oriented system identification approach](https://mlsanity.com/paper/2303.11197/) (2023)
- [Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study](https://mlsanity.com/paper/2403.14500/) (2024)
- [Reinforcement Learning for Motor Control: A Comprehensive Review](https://mlsanity.com/paper/2412.17936/) (2024)
- [In-Context Learning for Zero-Shot Speed Estimation of BLDC motors](https://mlsanity.com/paper/2504.00673/) (2025)

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

- [History, Adaptation, and Hidden Gain: Two Prospective Tests of Learned Predictive Control in a Synthetic Neural Population](https://mlsanity.com/paper/2610.14368/) (2026)

## Abstract (arXiv)

LLMs have shown strong in-context learning (ICL) abilities, but have not yet been extended to signal processing systems. Inspired by their design, we have proposed for the first time ICL using transformer models applicable to motor feedforward control, a critical task where classical PI and physics-based methods struggle with nonlinearities and complex load conditions. We propose a transformer based model architecture that separates signal representation from system behavior, enabling both few-shot finetuning and one-shot ICL. Pretrained on a large corpus of synthetic linear and nonlinear systems, the model learns to generalize to unseen system dynamics of real-world motors only with a handful of examples. In experiments, our approach generalizes across multiple motor load configurations, transforms untuned examples into accurate feedforward predictions, and outperforms PI controllers and physics-based feedforward baselines. These results demonstrate that ICL can bridge synthetic pretraining and real-world adaptability, opening new directions for data efficient control of physical systems.