用智能体协议加速核能审批，成本降七成，时间缩一半。
Overcoming the Regulatory Bottleneck via Agent-to-Agent Protocols: A Nuclear Case Study
- 设计代理间通信协议替代人工审批流程，保留关键决策的人类监督。
- 相比原流程节省2100万至4400万美元成本，缩短15个月时间。
- 适用于医药、环保、金融等需多方严格审核的高监管领域。
先进核反应堆设计的监管审查通常超过三年，耗资数亿美元。本文提出监管上下文协议（RCP），一种代理间通信标准，取代监管机构与申请方之间的正式人工沟通链，建立结构化、可审计的智能体通道，同时在安全关键节点保留人类监督。该协议基于对美国核能监管委员会1236份文件的分析，并通过多智能体原型验证。相较于8900万美元、42个月的重建基线，RCP使成本降低50%-77%（节省2100万-4400万美元），时间缩短65%（减少15个月）。独立运行的智能体仅实现5400万-7400万美元成本与21个月周期。剩余差距源于组织间流程的结构性瓶颈，唯有代理间协议方可压缩。类似瓶颈存在于药品审批、环境许可、金融监管与航空认证中。美国监管文书负担年机会成本达4265亿美元；若广泛复制，50%-77%的降幅将带来每年2100亿至3300亿美元的节约，接近美国GDP的1%。
原文摘要 · Abstract (English)
Regulatory review of advanced nuclear reactor designs routinely spans more than three years and consumes hundreds of millions of dollars in combined regulator and applicant labor. We present the Regulatory Context Protocol (RCP), an Agent-to-Agent communication standard that replaces the formal human-to-human pipeline between regulators and applicants with a structured, auditable agentic channel, while preserving human oversight at safety-significant decision points. The protocol is calibrated against an analysis of 1,236 documents from U.S. Nuclear Regulatory Commission advanced reactor dockets and demonstrated with a working multi-agent pilot. Against an 89M USD, 42-month Reconstructed Baseline, RCP cuts costs by 50-77 percent (21M-44M USD) and timelines by 65 percent (15 months). Without a shared protocol, Standalone Agents reach only 54M-74M USD and 21 months. The residual cost-and-time gap is structural, not algorithmic: it traces to the inter-organizational pipeline that only an agent-to-agent standard can compress. The same bottleneck - formal multi-party review under strict auditability requirements - characterizes pharmaceutical approvals, environmental permitting, financial supervision, and aviation certification. The US regulatory paperwork burden carries a 426.5 billion USD annual opportunity cost; replicated broadly, the projected 50-77 percent reduction implies savings on the order of 210-330 billion USD per year - approaching 1 percent of US GDP.
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