In clinical decision-making, predictive models face a persistent trade-off: accurate models are often opaque "black boxes," while interpretable methods frequently lack predictive precision or statistical grounding. In this paper, we challenge this dichotomy, positing that high predictive accuracy and high-quality descriptive explanations are not competing goals but synergistic outcomes of a deep, first-hand understanding of data. We propose the Reflective Cognitive Architecture (RCA), a novel framework designed to enable Large Language Models (LLMs) to learn directly from tabular data through experience and reflection. RCA integrates two core mechanisms: an iterative rules optimization process that refines logical argumentation by learning from prediction errors, and a distribution-aware rules check that grounds this logic in global statistical evidence to ensure robustness. We evaluated RCA against over 20 baselines - ranging from traditional machine learning to advanced reasoning LLMs and agents - across diverse medical datasets, including a proprietary real-world Catheter-Related Thrombosis (CRT) cohort. Crucially, to demonstrate real-world scalability, we extended our evaluation to two large-scale datasets. The results confirm that RCA achieves state-of-the-art predictive performance and superior robustness to data noise while simultaneously generating clear, logical, and evidence-based explanatory statements, maintaining its efficacy even at scale. The code is available at https://github.com/ssssszj/RCA.

翻译：在临床决策中，预测模型面临着一个持续的权衡：准确的模型往往是难以理解的“黑箱”，而可解释的方法则常常缺乏预测精度或统计依据。本文挑战这种二分法，提出高预测准确性与高质量描述性解释并非相互竞争的目标，而是对数据获得深刻、第一手理解的协同结果。我们提出了反思性认知架构（RCA），这是一个新颖的框架，旨在使大型语言模型（LLMs）能够通过经验和反思直接从表格数据中学习。RCA整合了两个核心机制：一个通过从预测错误中学习来优化逻辑论证的迭代规则优化过程，以及一个将这种逻辑建立在全局统计证据之上以确保鲁棒性的分布感知规则检查。我们在包括一个专有的真实世界导管相关血栓形成（CRT）队列在内的多样化医疗数据集上，将RCA与超过20个基线模型——从传统机器学习到先进的推理LLMs和智能体——进行了评估。至关重要的是，为了展示现实世界的可扩展性，我们将评估扩展到了两个大规模数据集。结果证实，RCA在实现最先进的预测性能和卓越的数据噪声鲁棒性的同时，能够生成清晰、逻辑性强且基于证据的解释性陈述，即使在大规模场景下也能保持其有效性。代码可在 https://github.com/ssssszj/RCA 获取。