SPEAR：智能合约审计中的多智能体协调工程案例研究
SPEAR: An Engineering Case Study of Multi-Agent Coordination for Smart Contract Auditing
AI总结：
SPEAR通过多智能体框架提升智能合约审计效率，采用风险感知启发式优先级分配与自动修复策略，实验证明其在协调性、恢复能力和资源利用方面的优势。
摘要：
我们提出了SPEAR，一种应用于真实安全分析流程中的多智能体协调框架，将已建立的MAS模式应用于智能合约审计。SPEAR将审计建模为由专门智能体协同执行的任务：规划代理使用风险感知启发式优先级合约，执行代理通过合同网协议分配任务，修复代理通过程序优先修复策略自动恢复脆弱生成物。智能体通过谈判和拍卖协议协调，并通过AGM兼容的修订更新局部信念。实证研究在受控故障场景下比较多智能体设计与集中式和流水线式替代方案，重点研究协调性、恢复行为和资源使用。
英文摘要：
We present SPEAR, a multi-agent coordination framework for smart contract auditing that applies established MAS patterns in a realistic security analysis workflow. SPEAR models auditing as a coordinated mission carried out by specialized agents: a Planning Agent prioritizes contracts using risk-aware heuristics, an Execution Agent allocates tasks via the Contract Net protocol, and a Repair Agent autonomously recovers from brittle generated artifacts using a programmatic-first repair policy. Agents maintain local beliefs updated through AGM-compliant revision, coordinate via negotiation and auction protocols, and revise plans as new information becomes available. An empirical study compares the multi-agent design with centralized and pipeline-based alternatives under controlled failure scenarios, focusing on coordination, recovery behavior, and resource use.
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