Leveraging Large Language Models to Bridge On-chain and Off-chain Transparency in Stablecoins

• URL: http://arxiv.org/abs/2512.02418v1
• Date: Tue, 02 Dec 2025 05:00:17 GMT
• Title: Leveraging Large Language Models to Bridge On-chain and Off-chain Transparency in Stablecoins
• Authors: Yuexin Xiang, Yuchen Lei, SM Mahir Shazeed Rish, Yuanzhe Zhang, Qin Wang, Tsz Hon Yuen, Jiangshan Yu,
• Abstract summary: We introduce a large language model (LLM)-based automated framework that bridges verifiable on-chain traces and off-chain disclosures locked in unstructured text.<n>Our findings show that LLM-assisted analysis enhances cross-modal transparency and supports automated, data-driven auditing in decentralized finance.
• Score: 10.666951732730665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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).

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