Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data, 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. Based on interviews, we propose TxSum, a new task and dataset of 100 complex Ethereum transactions annotated with natural-language summaries and step-wise semantic labels (intent, mechanism, etc.). We then introduce MATEX, a multi-agent system that emulates human experts' dual-process reasoning. MATEX achieves the highest faithfulness and intent clarity among strong baselines. It boosts user comprehension by 23.6% on complex transactions and doubles users' ability to find real attacks, significantly reducing blind signing.

翻译：理解以太坊交易的经济意图对于用户安全至关重要，然而现有工具仅暴露原始链上数据，导致普遍的“盲签”现象（在不理解交易内容的情况下予以批准）。通过对16位Web3用户的访谈，我们发现有效的解释应当具备结构化、风险感知能力，并基于代币流动层面。基于访谈，我们提出了TxSum——一项新任务及包含100笔复杂以太坊交易的数据集，这些交易均标注了自然语言摘要和分步语义标签（意图、机制等）。随后我们引入MATEX，这是一个模拟人类专家双过程推理机制的多智能体系统。在强基线模型中，MATEX实现了最高的忠实度和意图清晰度。该系统将用户对复杂交易的理解能力提升了23.6%，并将用户识别真实攻击的能力提高了一倍，从而显著减少了盲签行为。