RSI-Mol: Recursive Self-Improvement for Molecular Property Prediction

Although molecular property prediction has advanced substantially through large-scale pretraining and sophisticated neural architectures, most existing approaches remain static, with limited ability to learn from prediction failures and refine the task-relevant knowledge and representations that guide subsequent predictions. We argue that molecular property prediction can instead be formulated as recursive self-improvement, where execution feedback is used not only prediction outcomes but also the molecular reasoning state used to generate subsequent predictions. To this end, we introduce RSI-Mol, a recursive self-improving framework for molecular property prediction that iteratively evolves its molecular reasoning state through interactions between large language models (LLMs) and downstream predictive models. RSI-Mol realizes recursive self-improvement through two coupled processes: in a self-improvement proposal process, LLMs reason over task-specific knowledge and the current molecular reasoning state to propose task-relevant physicochemical features and predictive substructures for downstream prediction; in a recursive feedback update process, prediction errors and SHAP-based attribution signals produced by the downstream predictor are fed back into the reasoning process to assess the current proposals and guide the refinement, elimination, and discovery of molecular features and substructures. The resulting updated molecular reasoning state, including task features, predictive substructures, feature pools, and failure-case context, is then reused as the starting point of the next reasoning round, where a new downstream predictor is constructed and evaluated. In this way, execution outcomes from one iteration directly update the reasoning state that governs the next, forming a recursive loop of proposal generation, prediction, diagnosis, and state refinement. Beyond improving predictive performance, RSI-Mol provides interpretable molecular evidence through attribution-guided feedback and chemically meaningful substructure refinement. Extensive experiments on eight MoleculeNet benchmarks demonstrate that RSI-Mol consistently outperforms strong unimodal and multimodal baselines, achieving an average AUC of 81.21% across single-task and multi-task settings. Code and implementation details are available at https://anonymous.4open.science/r/RSI-Mol-E767/.