用概率推理框架提升大模型发现科学模型的效率与可解释性
A Probabilistic Framework for LLM-Based Model Discovery
- 将模型发现建模为从数据中采样机制模型的概率推断过程
- 在真实科学系统上实现可解释机制发现，提升预测准确性
- 适合从事科学建模、智能探索的科研人员参考
从观测数据自动发现机理模拟模型的方法为加速科学进展提供了新路径。这类方法通常采用类代理的迭代流程，模仿人类发现过程反复提出并修正候选模型。然而现有基于大语言模型（LLM）的方法多依赖手工设计的启发式规则，缺乏明确的概率建模。本文将模型发现重构为概率推断问题，即从能够解释数据的未知机理模型分布中进行采样。这一视角统一了模型提议、优化与选择的推理框架。作为具体实现，我们提出基于序贯蒙特卡洛（Sequential Monte Carlo）的ModelSMC算法：候选模型以粒子形式表示，由LLM迭代提出并优化，权重通过似然准则确定。在真实科学系统上的实验表明，该方法能发现具有可解释机制的模型，并改善后验预测检验。更广泛而言，这一概率视角为理解和开发基于大模型的模型发现方法提供了新范式。
原文摘要 · Abstract (English)
Automated methods for discovering mechanistic simulator models from observational data offer a promising path toward accelerating scientific progress. Such methods often take the form of agentic-style iterative workflows that repeatedly propose and revise candidate models by imitating human discovery processes. However, existing LLM-based approaches typically implement such workflows via hand-crafted heuristic procedures, without an explicit probabilistic formulation. We recast model discovery as probabilistic inference, i.e., as sampling from an unknown distribution over mechanistic models capable of explaining the data. This perspective provides a unified way to reason about model proposal, refinement, and selection within a single inference framework. As a concrete instantiation of this view, we introduce ModelSMC, an algorithm based on Sequential Monte Carlo sampling. ModelSMC represents candidate models as particles which are iteratively proposed and refined by an LLM, and weighted using likelihood-based criteria. Experiments on real-world scientific systems illustrate that this formulation discovers models with interpretable mechanisms and improves posterior predictive checks. More broadly, this perspective provides a probabilistic lens for understanding and developing LLM-based approaches to model discovery.
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