Stablecoins such as USDT and USDC aspire to peg stability by coupling issuance controls with reserve attestations. In practice, however, the transparency is split across two worlds: verifiable on-chain traces and off-chain disclosures locked in unstructured text that are unconnected. We introduce a large language model (LLM)-based automated framework that bridges these two dimensions by aligning on-chain issuance data with off-chain disclosure statements. First, we propose an integrative framework using LLMs to capture and analyze on- and off-chain data through document parsing and semantic alignment, extracting key financial indicators from issuer attestations and mapping them to corresponding on-chain metrics. Second, we integrate multi-chain issuance records and disclosure documents within a model context protocol (MCP) framework that standardizes LLMs access to both quantitative market data and qualitative disclosure narratives. This framework enables unified retrieval and contextual alignment across heterogeneous stablecoin information sources and facilitates consistent analysis. Third, we demonstrate the capability of LLMs to operate across heterogeneous data modalities in blockchain analytics, quantifying discrepancies between reported and observed circulation and examining their implications for cross-chain transparency and price dynamics. Our findings reveal systematic gaps between disclosed and verifiable data, showing that LLM-assisted analysis enhances cross-modal transparency and supports automated, data-driven auditing in decentralized finance (DeFi).

翻译：USDT和USDC等稳定币试图通过发行控制与储备证明相结合来维持锚定稳定性。然而在实际运作中，透明度被割裂在两个维度：可验证的链上交易轨迹与锁定在非结构化文本中且互不关联的链下披露信息。本文提出一种基于大型语言模型（LLM）的自动化框架，通过将链上发行数据与链下披露声明进行对齐，弥合这两个维度的鸿沟。首先，我们提出一个集成框架，利用LLM通过文档解析和语义对齐技术捕获并分析链上与链下数据，从发行方证明文件中提取关键财务指标，并将其映射至相应的链上度量标准。其次，我们在模型上下文协议（MCP）框架内整合多链发行记录与披露文档，该框架标准化了LLM对量化市场数据与定性披露叙述的访问接口。该框架实现了跨异构稳定币信息源的统一检索与上下文对齐，并支持一致性分析。第三，我们论证了LLM在区块链分析中处理异构数据模态的能力，量化报告流通量与实际观测值之间的差异，并检验其对跨链透明度与价格动态的影响。研究结果揭示了披露数据与可验证数据之间的系统性偏差，表明LLM辅助分析能增强跨模态透明度，并为去中心化金融（DeFi）领域实现自动化、数据驱动的审计提供支持。