NIMMGen: Learning Neural-Integrated Mechanistic Digital Twins with LLMs

• URL: http://arxiv.org/abs/2602.18008v1
• Date: Fri, 20 Feb 2026 05:46:54 GMT
• Title: NIMMGen: Learning Neural-Integrated Mechanistic Digital Twins with LLMs
• Authors: Zihan Guan, Rituparna Datta, Mengxuan Hu, Shunshun Liu, Aiying Zhang, Prasanna Balachandran, Sheng Li, Anil Vullikanti,
• Abstract summary: We introduce the Neural-Integrated Mechanistic Modeling (NIMM) evaluation framework to evaluate mechanistic models.<n>Our evaluation reveals fundamental challenges in current baselines, ranging from model effectiveness to code-level correctness.<n>We design NIMMgen, an agentic framework for neural-integrated mechanistic modeling that enhances code correctness and practical validity through iterative refinement.
• Score: 17.66806675891691
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mechanistic models encode scientific knowledge about dynamical systems and are widely used in downstream scientific and policy applications. Recent work has explored LLM-based agentic frameworks to automatically construct mechanistic models from data; however, existing problem settings substantially oversimplify real-world conditions, leaving it unclear whether LLM-generated mechanistic models are reliable in practice. To address this gap, we introduce the Neural-Integrated Mechanistic Modeling (NIMM) evaluation framework, which evaluates LLM-generated mechanistic models under realistic settings with partial observations and diversified task objectives. Our evaluation reveals fundamental challenges in current baselines, ranging from model effectiveness to code-level correctness. Motivated by these findings, we design NIMMgen, an agentic framework for neural-integrated mechanistic modeling that enhances code correctness and practical validity through iterative refinement. Experiments across three datasets from diversified scientific domains demonstrate its strong performance. We also show that the learned mechanistic models support counterfactual intervention simulation.

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