# Continuously Updating Digital Twins using Large Language Models

arXiv: https://arxiv.org/abs/2506.12091 · HTML: https://mlsanity.com/paper/2506.12091/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2025.

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

- [Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework](https://mlsanity.com/paper/2602.18008/) (2026)
- [Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure](https://mlsanity.com/paper/2606.18154/) (2026)

## Abstract (arXiv)

Digital twins are models of real-world systems that can simulate their dynamics in response to potential actions. In complex settings, the state and action variables, and available data and knowledge relevant to a system can constantly change, requiring digital twins to continuously update with these changes to remain relevant. Current approaches struggle in this regard, as they require fixed, well-defined modelling environments, and they cannot adapt to novel variables without re-designs, or incorporate new information without re-training. To address this, we frame digital twinning as an in-context learning problem using large language models, enabling seamless updates to the twin at inference time. We develop CALM-DT, a Context-Adaptive Language Model-based Digital Twin that can accurately simulate across diverse state-action spaces using in-context learning alone by utilising fine-tuned encoders for sample retrieval. We empirically demonstrate CALM-DT's competitive performance with existing digital twin approaches, and its unique ability to adapt to changes in its modelling environment without parameter updates.