CEAgent-GSL: Code-level Evolutionary Agent for Interpretable Graph Structure Learning on Omics

Abstract

Graph Structure Learning (GSL) is fundamental to effective graph-based modeling, particularly for life omics analysis. Traditional heuristics-based GSL methods impose overly rigid inductive biases, while optimization centric GSL suffers from non-convexity and inherent interpretability challenges. Evolutionary algorithms (EAs) have emerged as a promising alternative for their robust global exploration and ability to navigate discrete search spaces without gradient information. However, such stochastic mutations essentially bottleneck evolutionary efficiency. Furthermore, both optimization-based and existing evolutionary paradigms lack semantic guidance, resulting in inexplainable graph structures. Specifically, the topological modifications tend to be arbitrary, failing to reveal the underlying algorithmic logic required for biological insights. To address these problems, we propose CEAgent-GSL, a Code-level Evolutionary LLM Agent framework. CEAgent-GSL treats graph generation as an evolving population of executable scripts, leveraging an LLM for semantic mutation guided by task feedback. Recognizing that omics analysis demands not only high performance but also transparent and verifiable construction principles, we introduce Evolutionary Rule Abstraction to distill generalized logic from optimization trajectories, overcoming the opacity of traditional evolution. Furthermore, to ensure the scientific rigorousness of these discovered principles, we design a Counterfactual Validation Module to strictly verify the causal reliability of abstracted rules. Through this transparent evolutionary paradigm, CEAgent-GSL overcomes the limitations of conventional parametric tuning, and facilitates highly efficient and interpretable GSL, rigorously validated across 11 foundational bioinformatics algorithms on 10 diverse omics datasets. Besides individual task optimization, it can seamlessly integrate domain expertise with task-specific requirements by retrieving and adapting proven logic, thus generating customized rules for novel scenarios.