Problem
Research questions and friction points this paper is trying to address.
accountability
leak localization
water distribution networks
artificial intelligence
Verifiable abstention makes AI leak diagnosis accountable in water distribution networks
🤖 AI Summary
该研究通过可验证的弃权决策机制解决水网泄漏定位问题，结合物理基础执行器和大语言模型审计员提高决策精度。
📝 Abstract
Utilities lose a substantial share of treated water to leakage, yet rarely trust artificial-intelligence localizers to dispatch crews: guessing everywhere cannot justify excavation. The gap is accountability, not accuracy: no method proves when it should not act. Here we recast leak localization as decision-making under verifiable abstention. A physics-grounded executor agent falsifies hypotheses (leak, demand, sensor, valve) against a digital twin; an independent supervisor agent, with a large-language-model (LLM) auditor, checks evidence against a code-verifiable contract, then certifies a dispatch, requests evidence or abstains. Under field-grade noise, a 32% forced baseline becomes 96% decision precision on acted events. On an independently generated benchmark it acts on only 4 of 33 leaks, all correct. A 194-event register of audited real leak locations with twin-simulated pressures and flows yields five excavation dispatches, three correct, and 44% survey recovery at full district precision. Accountable abstention offers a defensible route to autonomous water-infrastructure operation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
verifiable abstention
digital twin
large-language-model auditor
decision precision
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