AI 中文总结
AbsorbEvo提出一种智能体框架，利用大语言模型推理和物理先验排序，实现微波吸收体的自然语言驱动自主逆向设计，在基准上显著提升任务成功率。
AI 中文摘要
设计高性能微波吸收体需要电磁理论、材料科学和仿真编程方面的专业知识，且涉及耗时的优化过程。在此，我们提出AbsorbEvo，一个用于自主逆向设计的智能体框架，该框架将自然语言性能目标转化为经全波仿真验证的设计。其候选进化策略整合了语言推理、基于物理的预测和历史反馈。大语言模型根据任务目标和计算历史提出参数调整的方向和幅度。该系统将定向增量与全局采样相结合以生成候选方案，并使用低成本预测模型作为物理先验对其进行排序。只有排名靠前的设计才进行全波仿真。通过物理有效性检查的结果用于评估性能并指导后续搜索。训练任务中的经验进一步提炼为文本技能，这些技能在新任务中使用前会经过独立验证。在保留的AbsorbBench-36任务上，在相同的提案预算下，AbsorbEvo实现了79.17%的任务成功率，而通用智能体为25.00%，随机搜索为12.50%。其平均最佳覆盖率为0.7816，而上述两种方法分别为0.6434和0.6448。通过将语言推理和基于物理的反馈整合到设计决策中，AbsorbEvo为自然语言驱动的微波吸收体自主逆向设计提供了方法论基础。
英文摘要
Designing high-performance microwave absorbers requires specialized expertise in electromagnetic theory, materials science and simulation programming, and entails time-consuming optimization. Here, we present AbsorbEvo, an agentic framework for autonomous inverse design that translates natural-language performance objectives into designs verified by full-wave simulations. Its candidate evolution strategy integrates language reasoning, physics-based prediction and historical feedback. A large language model proposes the directions and magnitudes of parameter adjustments based on task objectives and computational history. The system combines directed increments with global sampling to generate candidates and uses a low-cost predictive model as a physics prior to rank them. Only high-ranking designs undergo full-wave simulation. Results passing physical validity checks are used to evaluate performance and guide subsequent search. Experience from training tasks is further distilled into textual skills, which are independently validated before use in new tasks. Under identical proposal budgets on held-out AbsorbBench-36 tasks, AbsorbEvo achieved a task success rate of 79.17%, versus 25.00% for a generic agent and 12.50% for random search. Its mean best coverage was 0.7816, compared with 0.6434 and 0.6448, respectively. By integrating language reasoning and physics-based feedback into design decisions, AbsorbEvo provides a methodological foundation for natural-language-driven autonomous inverse design of microwave absorbers.
