多智能体AI系统中的自演化协调协议：一种探索性系统可行性研究
Self-Evolving Coordination Protocol in Multi-Agent AI Systems: An Exploratory Systems Feasibility Study
AI总结：
本研究探讨了自演化协调协议在多智能体系统中的可行性，通过实验展示有限自我修改在满足形式约束下的技术实现可能性。
摘要：
当代多智能体系统越来越多地依赖内部协调机制，以整合、仲裁或约束异构组件的输出。在金融等安全关键和受监管领域，这些机制必须满足严格的形式要求，保持可审计性，并在显式限定的范围内运行。因此，协调逻辑起着治理层的作用，而非优化启发式方法。本文提出了一种探索性系统可行性研究，研究自演化协调协议（SECP）：一种允许有限、经外部验证自我修改的协调协议，同时保持固定形式不变量。我们研究了一个受控的证明概念验证环境，在其中六个固定的拜占庭共识协议提案由六个专门的决策模块进行评估。所有协调制度均在相同的硬约束下运行，包括拜占庭容错（f < n/3）、O(n²)信息复杂度、完整的非统计安全性和活跃性论证，以及有界的可解释性。在单次设计中，比较了四种协调制度：全体硬否决、加权标量聚合、SECP v1.0（一种由智能体设计的非标量协议）以及SECP v2.0（一次受监管修改的结果）。结果通过单一指标评估，即提案覆盖率，定义为接受的提案数量。一次单一的递归修改将覆盖率从两个增加到三个接受的提案，同时保持所有声明的不变量。本研究不涉及统计显著性、最优性、收敛性或学习的主张。其贡献是架构性的：它证明了在显式形式约束下，受监管的协调协议有限自我修改是技术上可实现、可审计和可分析的，为受监管的多智能体系统奠定了基础。
英文摘要：
Contemporary multi-agent systems increasingly rely on internal coordination mechanisms to combine, arbitrate, or constrain the outputs of heterogeneous components. In safety-critical and regulated domains such as finance, these mechanisms must satisfy strict formal requirements, remain auditable, and operate within explicitly bounded limits. Coordination logic therefore functions as a governance layer rather than an optimization heuristic. This paper presents an exploratory systems feasibility study of Self-Evolving Coordination Protocols (SECP): coordination protocols that permit limited, externally validated self-modification while preserving fixed formal invariants. We study a controlled proof-of-concept setting in which six fixed Byzantine consensus protocol proposals are evaluated by six specialized decision modules. All coordination regimes operate under identical hard constraints, including Byzantine fault tolerance (f < n/3), O(n2) message complexity, complete non-statistical safety and liveness arguments, and bounded explainability. Four coordination regimes are compared in a single-shot design: unanimous hard veto, weighted scalar aggregation, SECP v1.0 (an agent-designed non-scalar protocol), and SECP v2.0 (the result of one governed modification). Outcomes are evaluated using a single metric, proposal coverage, defined as the number of proposals accepted. A single recursive modification increased coverage from two to three accepted proposals while preserving all declared invariants. The study makes no claims regarding statistical significance, optimality, convergence, or learning. Its contribution is architectural: it demonstrates that bounded self-modification of coordination protocols is technically implementable, auditable, and analyzable under explicit formal constraints, establishing a foundation for governed multi-agent systems.
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