Human-level concept learning argues that humans typically learn new concepts from a single example, whereas machine learning algorithms typically require hundreds of samples to learn a single concept. Our brain subconsciously identifies important features and learns more effectively. Contribution: In this paper, we present the Coordinate Matrix Machine (CM$^2$). This purpose-built small model augments human intelligence by learning document structures and using this information to classify documents. While modern "Red AI" trends rely on massive pre-training and energy-intensive GPU infrastructure, CM$^2$ is designed as a Green AI solution. It achieves human-level concept learning by identifying only the structural "important features" a human would consider, allowing it to classify very similar documents using only one sample per class. Advantage: Our algorithm outperforms traditional vectorizers and complex deep learning models that require larger datasets and significant compute. By focusing on structural coordinates rather than exhaustive semantic vectors, CM$^2$ offers: 1. High accuracy with minimal data (one-shot learning) 2. Geometric and structural intelligence 3. Green AI and environmental sustainability 4. Optimized for CPU-only environments 5. Inherent explainability (glass-box model) 6. Faster computation and low latency 7. Robustness against unbalanced classes 8. Economic viability 9. Generic, expandable, and extendable

翻译：人类级概念学习认为，人类通常能从单个示例中学习新概念，而机器学习算法通常需要数百个样本才能学习单一概念。我们的大脑会潜意识地识别重要特征并更有效地学习。贡献：本文提出坐标矩阵机（CM$^2$）。这一专用小型模型通过学习文档结构并利用该信息对文档进行分类，从而增强人类智能。当现代“红色人工智能”趋势依赖大规模预训练和能耗密集的GPU基础设施时，CM$^2$被设计为一种绿色人工智能解决方案。它通过仅识别人类会考虑的结构性“重要特征”，实现了人类级概念学习，使其能够每类仅使用一个样本即可分类高度相似的文档。优势：我们的算法优于需要更大数据集和大量计算的传统向量化器与复杂深度学习模型。通过专注于结构坐标而非穷举的语义向量，CM$^2$提供：1. 最小数据下的高准确率（单样本学习） 2. 几何与结构智能 3. 绿色人工智能与环境可持续性 4. 专为纯CPU环境优化 5. 固有的可解释性（透明盒模型） 6. 更快的计算与低延迟 7. 对不平衡类别的鲁棒性 8. 经济可行性 9. 通用、可扩展与可拓展性