SR-LLM: An incremental symbolic regression framework driven by LLM-based retrieval-augmented generation.

ABSTRACT: Symbolic regression (SR) has regained research prominence as deep learning advancements accelerate the search for analytical models from observational data. However, the vast search space often hinders existing algorithms to yield complex analytical expressions. We present SR-LLM, an SR framework integrating retrieval-augmented generation mechanisms based on large language models (LLM) to achieve incremental learning. Specifically, our framework is capable of leveraging accumulated prior knowledge and past exploration results from external knowledge bases to retrieve the most relevant information for current regression tasks. It first composes prior information into small symbolic groups with the assistance of the LLMs and then utilizes deep reinforcement learning to combine these groups t

SUBMITTER: Guo Z 

PROVIDER: S-EPMC12772181 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature