Text-to-ES aims to translate natural language into executable Elasticsearch(ES) queries. Unlike more open-ended reasoning tasks, such as mathematical reasoning, this problem is defined over a constrained domain-specific language, where the synergy between forward reasoning and backward reasoning can be more explicitly realized under strict structural constraints. While recent work has explored reasoning-oriented training and prompting in text-to-SQL, forward and backward reasoning have not been systematically studied in text-to-ES. In this paper, we systematically study the bidirectional duality of the text-to-ES task by establishing a bidirectional reasoning perspective across both training and prompting stages. We introduce two large-scale datasets, TERD and ETRD, for forward and backward reasoning, respectively, and propose DualES, a unified framework that combines the two. Experimental results show that our DualES-CoRE strategy sets a new State-of-the-Art(SOTA), achieving 69.23% on LED and 50.14% on BirdES. This corresponds to absolute execution accuracy gains of 6.92% and 7.34% over previous benchmarks, outperforming both open-source and closed-source baselines. We will release the datasets in the future.

Medical insurance plays a pivotal role in the healthcare system, where effective supervision relies on the precise execution of complex medical insurance rules. Existing solutions for medical insurance violation detection mainly rely on costly manual labor, limiting efficiency. Although current Large Language Models (LLMs) have demonstrated significant potential in general Natural Language Processing (NLP) tasks, they still struggle to understand these rules, and a corresponding evaluation benchmark is lacking. Hence, we propose T2ER, a benchmark to evaluate the capability of LLMs in converting texts into executable rules in medical insurance. T2ER consists of 2,115 rules and 5,636 synthetic Electronic Medical Record (EMR) test cases for execution verification. Furthermore, we design a dual evaluation framework spanning rule and execution levels to comprehensively assess transformation quality. We conduct extensive experiments on advanced LLMs. The results reveal a significant human-AI performance gap, indicating that this conversion remains a formidable challenge. The dataset is available at https://github.com/YHJ1223/T2ER.