NIMMGen: Learning Neural-Integrated Mechanistic Digital Twins with LLMs

TL;DR

NIMMGen introduces a framework for evaluating and improving LLM-generated mechanistic models in realistic scientific settings, addressing reliability and practical validity issues.

Contribution

The paper presents NIMMGen, a novel agentic framework that enhances the accuracy and robustness of neural-integrated mechanistic models generated by LLMs.

Findings

NIMMGen outperforms existing baselines across multiple datasets.

Models support counterfactual intervention simulation.

Framework improves code correctness and practical validity.

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.