Paper 10Attention Alone is Not Enough: Competitive Cost Discovery as the Organizing Principle for Next-Generation AI

In March 2026, multiple research teams identified "depth degradation" as a structural flaw in the Transformer architecture, where a significant percentage of layers perform near-identity transformations. This paper argues that depth degradation is a symptom of a deeper pathology: static resource allocation. We demonstrate that the rules governing resource distribution across modules currently do not participate in the learning process. Drawing on our theory of "Competitive Cost Discovery," we establish that the cost information required for intelligent allocation is inherently opaque to any centralized observer. We prove that competition is not merely an efficiency tool but an epistemological mechanism—the only process capable of discovering the true cost structure of a modular system. We propose replacing static residual connections and gated attention with a competitive allocation layer, transforming the Transformer from a static pipeline into an adaptive, self-organizing economy of computation. 2026 年 3 月,多个研究团队相继发现 Transformer 架构存在“深度退化”这一结构性缺陷,即大量层级仅执行近乎恒等变换。本文指出,深度退化本质上是“静态资源分配”这一深层病理的症状:目前主导模块间资源分配的规则并未参与学习过程。 借鉴“竞争性成本发现”理论,我们证明了智能化分配所需的成本信息对于任何中心化观察者而言本质上是不透明的。我们论证了竞争不仅是效率工具,更是一种认识论机制——它是发现模块化系统真实成本结构的唯一过程。我们提议用竞争性分配层取代传统的静态残差连接和门控注意力机制,将 Transformer 从静态流水线转变为自适应、自组织的计算经济体。