MATEX: A Multi-Agent Framework for Explaining Ethereum Transactions
By: Zifan Peng
Potential Business Impact:
Explains tricky online money transfers clearly.
Understanding a complicated Ethereum transaction remains challenging: multi-hop token flows, nested contract calls, and opaque execution paths routinely lead users to blind signing. Based on interviews with everyday users, developers, and auditors, we identify the need for faithful, step-wise explanations grounded in both on-chain evidence and real-world protocol semantics. To meet this need, we introduce (matex, a cognitive multi-agent framework that models transaction understanding as a collaborative investigation-combining rapid hypothesis generation, dynamic off-chain knowledge retrieval, evidence-aware synthesis, and adversarial validation to produce faithful explanations.
Similar Papers
Know Your Intent: An Autonomous Multi-Perspective LLM Agent Framework for DeFi User Transaction Intent Mining
Artificial Intelligence
Helps understand why people use digital money.
DeepTx: Real-Time Transaction Risk Analysis via Multi-Modal Features and LLM Reasoning
Cryptography and Security
Stops fake online scams before you click.
DeepTx: Real-Time Transaction Risk Analysis via Multi-Modal Features and LLM Reasoning
Cryptography and Security
Stops online scams before you click.