让智能体通过敏感度共享实现高效协同，避免群体决策失灵。
Ripple Effect Protocol: Coordinating Agent Populations
- 智能体不仅传决策，还传对环境变化的敏感度信号。
- 在三个场景中，协调准确率和效率比A2A提升41%至100%。
- 适合大规模智能体协同，尤其适用于复杂网络与多模态大模型。
现代智能体使用A2A、ACP等协议通信，但这些机制侧重沟通而非协调。随着智能体数量增加，个体智能反而导致群体表现脆弱，集体结果不佳。本文提出涟漪效应协议（Ripple Effect Protocol, REP），使智能体在传递决策的同时，也共享轻量级敏感度信号——即其选择随关键环境变量变化的预期响应。这些敏感度信号在局部网络中传播，推动群体更快更稳定地达成一致。我们形式化了REP的协议规范，将消息结构与可选聚合规则分离，并在多种激励和网络拓扑下评估其性能。在三个领域测试：(i) 供应链级联（啤酒游戏），(ii) 稀疏网络中的偏好聚合（电影排期），(iii) 可持续资源分配（渔场）。结果表明，REP相比A2A在协调准确率和效率上提升41%至100%，且能灵活处理来自大语言模型的多模态敏感度信号。通过将协调作为协议层能力，REP为新兴的智能体互联网提供可扩展基础设施。
原文摘要 · Abstract (English)
Modern AI agents can exchange messages using protocols such as A2A and ACP, yet these mechanisms emphasize communication over coordination. As agent populations grow, this limitation produces brittle collective behavior, where individually smart agents converge on poor group outcomes. We introduce the Ripple Effect Protocol (REP), a coordination protocol in which agents share not only their decisions but also lightweight sensitivities - signals expressing how their choices would change if key environmental variables shifted. These sensitivities ripple through local networks, enabling groups to align faster and more stably than with agent-centric communication alone. We formalize REP's protocol specification, separating required message schemas from optional aggregation rules, and evaluate it across scenarios with varying incentives and network topologies. Benchmarks across three domains: (i) supply chain cascades (Beer Game), (ii) preference aggregation in sparse networks (Movie Scheduling), and (iii) sustainable resource allocation (Fishbanks) show that REP improves coordination accuracy and efficiency over A2A by 41 to 100%, while flexibly handling multimodal sensitivity signals from LLMs. By making coordination a protocol-level capability, REP provides scalable infrastructure for the emerging Internet of Agents
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