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

2025-12-02

AI Summary

Stablecoins (e.g., USDT, USDC) suffer from a “transparency fragmentation” problem—namely, a semantic disconnect between on-chain transaction data and off-chain reserve disclosures. To address this, we propose the Model-Context Protocol (MCP), the first LLM-driven cross-modal analytical framework that unifies multi-chain on-chain issuance data with unstructured off-chain disclosure texts to enable automatic semantic alignment and mapping of financial metrics. Our methodology integrates document parsing, multi-chain data ingestion, and LLM-based reasoning to jointly audit quantitative circulation metrics and qualitative disclosure narratives. Experiments systematically quantify, for the first time, the “disclosure–circulation deviation” across major stablecoins on Ethereum, Solana, and other chains. Results demonstrate the efficacy and scalability of LLMs in enhancing DeFi transparency and enabling automated, compliance-aware auditing.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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).

Problem

• Bridging on-chain and off-chain transparency gaps in stablecoins
• Automating alignment of issuance data with disclosure statements using LLMs
• Quantifying discrepancies between reported and observed stablecoin circulation

Innovation

• LLM framework aligns on-chain data with off-chain disclosures
• MCP standardizes access to quantitative and qualitative stablecoin information
• LLMs quantify discrepancies between reported and observed circulation