把算法直接编码进Transformer参数，新手也能快速上手。
The Transformer Cookbook
- 将算法转化为Transformer层的参数配置，实现端到端编码。
- 从基础算术到复杂数据路由，完整覆盖各类算法实现。
- 适合初学者入门与专家参考，统一整理零散研究成果。
我们提出Transformer食谱：一套将算法直接编码至Transformer参数中的技术集合。该工作旨在解决此类研究的陡峭学习曲线问题，该问题因文献碎片化而加剧，关键成果分散于众多论文中。本文将这些分散的研究成果整合为一系列精选配方，展示如何在前馈层中实现基础算术，通过自注意力机制完成复杂数据路由。这套系统化的公式整理，既为初学者提供易入门的起点，也为专家提供系统性参考。统一呈现的Transformer构造方法，为未来研究奠定了基础，涵盖计算复杂性理论、架构设计与可解释性等方向。
原文摘要 · Abstract (English)
We present the transformer cookbook: a collection of techniques for directly encoding algorithms into a transformer's parameters. This work addresses the steep learning curve of such endeavors, a problem exacerbated by a fragmented literature where key results are scattered across numerous papers. In particular, we synthesize this disparate body of findings into a curated set of recipes that demonstrate how to implement everything from basic arithmetic in feed-forward layers to complex data routing via self-attention. Our mise en place of formulations is for both newcomers seeking an accessible entry point and experts in need of a systematic reference. This unified presentation of transformer constructions provides a foundation for future work spanning theoretical research in computational complexity to empirical investigations in architecture design and interpretability.
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