Despite the number of successful applications of the Extreme Learning Machine
(ELM), we show that its underlying foundational principles do not have a
rigorous mathematical justification. Specifically, we refute the proofs of two
main statements, and we also create a dataset that provides a counterexample to
the ELM learning algorithm and explain its design, which leads to many such
counterexamples. Finally, we provide alternative statements of the foundations,
which justify the efficiency of ELM in some theoretical cases.

尽管极限学习机在许多应用中取得了成功，但我们发现其基本原理缺乏严格的数学证明。具体而言，我们推翻了两个主要陈述的证明，并创建了一个数据集来提供 ELM 学习算法的反例，并解释其设计，这引发了许多这样的反例。最后，我们提供了基础的替代陈述，以在一些理论情况下证明 ELM 的效率。

极限学习机理论框架的关键分析

A Critical Analysis of the Theoretical Framework of the Extreme Learning  Machine

This paper introduces the Multi-Step Action Model (MSAM), a closed-source AI
model designed by Empsing to address challenges hindering AI adoption in
enterprises. Through a holistic examination, this paper explores MSAM's
foundational principles, design architecture, and future trajectory. It
evaluates MSAM's performance via rigorous testing methodologies and envisions
its potential impact on advancing AI adoption within organizations.

介绍了 Empsing 设计的闭源 AI 模型 —— 多步骤行动模型（MSAM），以解决妨碍企业 AI 采用的挑战。通过全面审查，探讨了 MSAM 的基本原理、设计架构和未来发展。通过严格的测试方法评估 MSAM 的性能，并设想其对推进组织内 AI 采用的潜在影响。