利用大型语言模型弥合稳定币跨领域透明度
Leveraging Large Language Models to Bridge Cross-Domain Transparency in Stablecoins
AI总结：
本文提出利用大型语言模型整合稳定币跨领域透明度，通过语义对齐发行披露与市场数据，揭示披露与可验证数据间的系统性差距，提升去中心化金融的自动化审计能力。
摘要：
稳定币如USDT和USDC通过发行控制与储备证明来维持稳定。然而，实际透明度在异构数据源中碎片化，关键证据如流通、储备和披露分散在难以联合连接和解释的记录中。我们引入基于大型语言模型（LLM）的自动化框架，通过对齐发行人披露与可观察流通证据来弥合稳定币的跨领域透明度。首先，我们提出使用LLM的整合框架，解析文档、提取显著的财务指标，并语义对齐报告陈述与相应的市场和发行指标。其次，我们将多链发行记录和披露文档整合到模型上下文协议（MCP）框架中，该框架标准化LLM对定量市场数据和定性披露叙述的访问。此框架实现了异构稳定币信息源的统一检索和上下文对齐，并促进了一致分析。第三，我们展示了LLM在区块链分析中跨异构数据域操作的能力，量化报告与观察流通之间的差异，并探讨其对透明度和价格动态的影响。我们的发现揭示了披露与可验证数据之间的系统性差距，表明LLM辅助分析增强了跨领域透明度，并支持去中心化金融（DeFi）中的自动化、数据驱动的审计。
英文摘要：
Stablecoins such as USDT and USDC aspire to peg stability by coupling issuance controls with reserve attestations. In practice, however, transparency remains fragmented across heterogeneous data sources, with key evidence about circulation, reserves, and disclosure dispersed across records that are difficult to connect and interpret jointly. We introduce a large language model (LLM)-based automated framework for bridging cross-domain transparency in stablecoins by aligning issuer disclosures with observable circulation evidence. First, we propose an integrative framework using LLMs to parse documents, extract salient financial indicators, and semantically align reported statements with corresponding market and issuance metrics. Second, we integrate multi-chain issuance records and disclosure documents within a model context protocol (MCP) framework that standardizes LLM 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 domains in blockchain analytics, quantifying discrepancies between reported and observed circulation and examining their implications for transparency and price dynamics. Our findings reveal systematic gaps between disclosed and verifiable data, showing that LLM-assisted analysis enhances cross-domain transparency and supports automated, data-driven auditing in decentralized finance (DeFi).
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