EvoMol: Semantic Gradient Evolution for Rule-Guided Molecular Reasoning

Large language models (LLMs) have shown strong potential for molecular reasoning, but their performance depends heavily on the reasoning context, and recurring errors are rarely retained as reusable strategies. We present EvoMol, a semantic gradient evolution framework that adapts the reasoning logic of frozen LLMs without updating model parameters. EvoMol represents the task context as a structured logic kernel containing task decomposition strategies, molecular constraints, and mechanistic heuristics. At each generation, EvoMol diagnoses model failures and organizes them as Actionable Side Information, including the observed error, underlying chemical principle, corrective action, and diagnostic confidence. This structured feedback guides rule-level mutations of the logic kernel. A multi-frontier Pareto consensus mechanism then selects candidates that perform consistently across instances and objectives. Across three molecular benchmarks and Qwen2.5 models from 3B to 14B parameters, EvoMol achieves the best performance in 49 of 60 settings under limited supervision and outperforms direct prompting and GRPO in most cases. Controlled analyses show that the evolved kernels capture task-relevant strategies beyond generic or irrelevant prompt content and transfer across mechanistically related tasks. EvoMol also improves multi-objective molecular optimization under competing property constraints. These results demonstrate that EvoMol provides a data-efficient and inspectable approach to adapting molecular reasoning with frozen LLMs.