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arXiv · 2608.18836

Verifiable abstention makes AI leak diagnosis accountable in urban water distribution networks

Tianwei Mu·Yue Wang·Mingzhe Yuan·Manhong Huang·Wenhong Wang·Xuerui Yin·Qing Luo·Min Xiao·Hui Yang·Jun Li·Dan Xue
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Abstract

Leak localization is usually evaluated as forced-choice prediction, although sparse hydraulic observations may not justify excavation. Here, we quantify a pressure-information limit and use it to recast localization as selective, evidence-gated decision-making. A physics-grounded executor falsifies competing leak, demand, sensor and valve hypotheses in a hydraulic twin. Deterministic code computes every number and every acceptance predicate; an independent large language model auditor may add a rejection but never overturn a failed check. Forced retrieval placed only 95 of 300 leaks in the correct zone. Across 550 mixed events, the gate acted on 223 (214 correct); on a third-party 33-leak benchmark, all four accepted events were correct. In a replay of 194 audited City D repairs, the pressure tier authorized five excavation recommendations, three matching the repaired district, while the district-inflow tier returned the correct district for 85 events. Observability limits with machine-checkable abstention enable auditable utility intervention.

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Tianwei Mu, Yue Wang, Mingzhe Yuan, Manhong Huang, Wenhong Wang, Xuerui Yin, Qing Luo, Min Xiao, Hui Yang, Jun Li, Dan Xue. 2026-08-19. Verifiable abstention makes AI leak diagnosis accountable in urban water distribution networks. https://arxiv.org/abs/2608.18836

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