We present NormCode Canvas (v1.1.3), a deployed system realizing Case-Based Reasoning at two levels for multi-step LLM workflows. The foundation is NormCode, a semi-formal planning language whose compiler-verified scope rule ensures every execution checkpoint is a genuinely self-contained case -- eliminating the implicit shared state that makes retrieval unreliable and failure non-localizable in standard orchestration frameworks. Level 1 treats each checkpoint as a concrete case (suspended runtime); Fork implements retrieve-and-reuse, Value Override implements revision with automatic stale-boundary propagation. Level 2 treats each compiled plan as an abstract case; the compilation pipeline is itself a NormCode plan, enabling recursive case learning. Three structural properties follow: (C1) direct checkpoint inspection; (C2) pre-execution review via compiler-generated narrative; (C3) scope-bounded selective re-execution. Four deployed plans serve as structured evidence: PPT Generation produces presentation decks at ~40s per slide on commercial APIs; Code Assistant carries out multi-step software-engineering tasks spanning up to ten reasoning cycles; NC Compilations converts natural-language specifications into executable NormCode plans; and Canvas Assistant, when connected to an external AI code editor, automates plan debugging. Together these plans form a self-sustaining ecosystem in which plans produce, debug, and refine one another -- realizing cumulative case-based learning at system scale.

翻译：本文提出NormCode Canvas（v1.1.3），这是一个已部署的系统，通过在两个层级实现基于案例的推理来支持多步骤大语言模型工作流。其基础是NormCode——一种半形式化的规划语言，其编译器验证的作用域规则确保每个执行检查点都是一个真正自包含的案例，从而消除了标准编排框架中导致检索不可靠且故障难以定位的隐式共享状态。层级1将每个检查点视为具体案例（暂停的运行时）；Fork操作实现检索与复用，Value Override操作通过自动过时边界传播实现案例修订。层级2将每个编译后的计划视为抽象案例；编译流水线本身即是一个NormCode计划，从而支持递归式案例学习。由此衍生出三项结构特性：（C1）可直接检查检查点；（C2）通过编译器生成的叙述实现执行前审查；（C3）作用域限定的选择性重新执行。四项已部署计划构成结构化实证：PPT Generation在商业API上以每页约40秒的速度生成演示文稿；Code Assistant执行跨越多达十个推理周期的多步骤软件工程任务；NC Compilations将自然语言规约转换为可执行的NormCode计划；Canvas Assistant在连接外部AI代码编辑器时可自动化计划调试。这些计划共同构成了一个自我维持的生态系统，其中计划相互生成、调试与优化——在系统尺度上实现了累积式的基于案例的学习。