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This paper introduces MEDA, an LLM- and symbolic regression-powered agentic framework for automatically discovering ordinary differential equation (ODE) models of biological dynamical systems. It retrieves background knowledge, proposes candidate ODEs, and evaluates them across canonical model retrieval, extrapolation, and open-ended discovery tasks, demonstrating strong structural recovery and biologically plausible models.
PyCC.id is a Python library for hypothesis-driven equation discovery from time-series data, leveraging structural identifiability to help filter candidate models.
This paper introduces DoLQ, a multi-agent framework that uses Large Language Models to perform both qualitative and quantitative evaluations for discovering ordinary differential equations from observational data.