Spec2Twin-Chain：利用大型语言模型编排双层优化以构建区块链数字孪生

中文摘要 AI 辅助

构建区块链数字孪生在很大程度上需要将领域知识和特定系统描述转换为模拟器架构，根据行为证据校准其参数，并验证所构建的孪生体。这些步骤通常通过特定应用的建模工作来完成，这类工作难以在不同系统和下游决策问题中复用。我们考虑通过Spec2Twin-Chain实现这一过程的自动化，该框架将区块链数字孪生的构建形式化为一个双层优化问题。在上层，大型语言模型（LLM）利用系统规格、行为证据以及已评估设计的反馈来提出和修改结构上可接受的架构。在下层，基于仿真的优化器在明确目标和约束条件下校准架构相关的参数。两个层级进行迭代，下层评估的候选方案会被保留在全局档案中，用于指导上层后续的方案提出。我们开展了涉及孪生体校准、反馈驱动恢复、压力分析、下游策略优化和策略更新的受控实验。结果表明，该框架能够构建行为准确的孪生体，通过迭代反馈改进初始设计，并复用已校准的孪生体以支持下游决策。

英文摘要

Building a blockchain digital twin largely requires translating domain knowledge and specific system descriptions into a simulator architecture, calibrating its parameters against behavioral evidence, and validating the constructed twin. These steps are commonly performed through application-specific modeling efforts that can be difficult to reuse across systems and downstream decision problems. We consider automating this process through Spec2Twin-Chain, a framework that formulates blockchain digital-twin construction as a bi-level optimization problem. At the upper level, a large language model proposes and revises structurally admissible architectures using system specifications, behavioral evidence, and feedback from evaluated designs. At the lower level, a simulation-based optimizer calibrates the architecture-conditioned parameters under explicit objectives and guardrail constraints. The two levels iterate. The evaluated candidates at lower levels are retained in a global archive and used to guide subsequent proposals at upper levels. We conduct controlled experiments involving twin calibration, feedback-driven recovery, stress analysis, downstream policy optimization, and policy updating. The results demonstrate that the framework can construct behaviorally accurate twins, improve initial designs through iterative feedback, and reuse calibrated twins to support downstream decisions.

发表机构

• University of California, Berkeley（加利福尼亚大学伯克利分校）
• Columbia University（哥伦比亚大学）

机构由 AI 辅助整理，请以论文原文为准。