Continuously Updating Digital Twins using Large Language Models

• URL: http://arxiv.org/abs/2506.12091v1
• Date: Wed, 11 Jun 2025 14:45:28 GMT
• Title: Continuously Updating Digital Twins using Large Language Models
• Authors: Harry Amad, Nicolás Astorga, Mihaela van der Schaar,
• Abstract summary: Digital twins are models of real-world systems that can simulate their dynamics in response to potential actions.<n>Current approaches struggle in this regard, as they require fixed, well-defined modelling environments.<n>We develop CALM-DT, a Context-Adaptive Language Model-based Digital Twin that can accurately simulate across diverse state-action spaces.
• Score: 49.7719149179179
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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