TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding

Abstract:Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data or surface-level intent, leading to widespread ``blind signing'' (approving transactions without understanding them). Through interviews with 16 Web3 users, we find that effective explanations should be structured, risk-aware, and grounded at the token-flow level. Motivated by these findings, we formulate TxSum, a new domain-grounded NLP task for DeFi transaction explanation, and construct a dataset of 187 complex Ethereum transactions with 2,375 token-flow annotations and transaction-level summaries. We further introduce MATEX, a grounded multi-agent framework for high-stakes transaction explanation. It selectively retrieves external knowledge under uncertainty and audits explanations against raw traces to improve token-flow-level factual consistency. MATEX achieves the strongest overall explanation quality, especially on micro-level factuality and intent quality. It improves user comprehension on complex transactions from 52.9% to 76.5% over the strongest baseline and raises malicious-transaction rejection from 36.0% to 88.0%, while maintaining a low false-rejection rate on benign transactions.

Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)

arXiv:2512.06933 [cs.CE]

(or arXiv:2512.06933v4 [cs.CE] for this version)

https://doi.org/10.48550/arXiv.2512.06933 Focus to learn more arXiv-issued DOI via DataCite

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