这篇论文打破了‘解释会牺牲准确率’的旧观念，用RashomonLLM让大模型自动生成解释还能反过来提升预测，三个真实场景实测都更强，研究很扎实。
论文提出Rashomon解释范式，构建一组忠实预测的解释而非单个。提出RashomonLLM框架，使用解释-预测-反思代理工作流，在客户流失分类、临床生存回归和工业点击预测三个任务上验证。RashomonLLM在准确率和解释质量上均显著优于SOTA基线，增益由解释忠实性驱动。该方法对分布偏移、时间分割和随机种子鲁棒。
All Explanations are Wrong, But Many Are Useful: Exploring the Rashomon Explanation Set with Large Language Models
Explaining machine-learning models is increasingly important for decision-making and consumer trust, yet it is widely believed to come at a cost: existing Explainable AI (XAI) methods suffer from a persistent accuracy-explainability trade-off. We argue that this trade-off is not fundamental, but an artifact of treating explanation and prediction as separate objectives; when properly coupled, they become complementary, so that equipping a model to explain itself improves, rather than degrades, its accuracy. We introduce the Rashomon Explanation paradigm, which builds a set of faithful, prediction-guiding explanations rather than a single one, and prove that this set is generally non-empty and that explanation fidelity bounds the performance of the models it guides. To explore this set, we propose RashomonLLM, an Explanation-Prediction-Reflection agentic workflow that generates explanations in natural language by iteratively aligning them with predictions, and we prove it converges and recovers the full set. Across customer-churn classification, clinical survival regression, and industrial click-through prediction on large-scale live-streaming logs, RashomonLLM significantly outperforms state-of-the-art prediction and XAI baselines on both accuracy and explanation quality, with gains driven by explanation fidelity and robust to distribution shifts, temporal splits, and seeds. Our framework thus advances business performance while laying the groundwork for consumer trust.